Category Archives: Protein Folding

Distributed computing project simulating protein folding

Folding@home (FAH or F@h) is a distributed computing project for disease research that simulates protein folding, computational drug design, and other types of molecular dynamics. The project uses the idle processing resources of thousands of personal computers owned by volunteers who have installed the software on their systems. Its main purpose is to determine the mechanisms of protein folding, which is the process by which proteins reach their final three-dimensional structure, and to examine the causes of protein misfolding. This is of significant academic interest with major implications for medical research into Alzheimer's disease, Huntington's disease, and many forms of cancer, among other diseases. To a lesser extent, Folding@home also tries to predict a protein's final structure and determine how other molecules may interact with it, which has applications in drug design. Folding@home is developed and operated by the Pande Laboratory at Stanford University, under the direction of Prof. Vijay Pande, and is shared by various scientific institutions and research laboratories across the world.[4]

The project has pioneered the use of graphics processing units (GPUs), PlayStation3s, Message Passing Interface (used for computing on multi-core processors), and some Sony Xperia smartphones for distributed computing and scientific research. The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods.[5] As part of the clientserver model network architecture, the volunteered machines each receive pieces of a simulation (work units), complete them, and return them to the project's database servers, where the units are compiled into an overall simulation. Volunteers can track their contributions on the Folding@home website, which makes volunteers' participation competitive and encourages long-term involvement.

Folding@home is one of the world's fastest computing systems, with a speed of approximately 98.7petaFLOPS[6] as of June 2019[update]. This performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved. Since its launch on 1Oct2000, the Pande Lab has produced 212 scientific research papers as a direct result of Folding@home.[7] Results from the project's simulations agree well with experiments.[8][9][10]

Proteins are an essential component to many biological functions and participate in virtually all processes within biological cells. They often act as enzymes, performing biochemical reactions including cell signaling, molecular transportation, and cellular regulation. As structural elements, some proteins act as a type of skeleton for cells, and as antibodies, while other proteins participate in the immune system. Before a protein can take on these roles, it must fold into a functional three-dimensional structure, a process that often occurs spontaneously and is dependent on interactions within its amino acid sequence and interactions of the amino acids with their surroundings. Protein folding is driven by the search to find the most energetically favorable conformation of the protein, i.e., its native state. Thus, understanding protein folding is critical to understanding what a protein does and how it works, and is considered a holy grail of computational biology.[11][12] Despite folding occurring within a crowded cellular environment, it typically proceeds smoothly. However, due to a protein's chemical properties or other factors, proteins may misfold, that is, fold down the wrong pathway and end up misshapen. Unless cellular mechanisms can destroy or refold misfolded proteins, they can subsequently aggregate and cause a variety of debilitating diseases.[13] Laboratory experiments studying these processes can be limited in scope and atomic detail, leading scientists to use physics-based computing models that, when complementing experiments, seek to provide a more complete picture of protein folding, misfolding, and aggregation.[14][15]

Due to the complexity of proteins' conformation or configuration space (the set of possible shapes a protein can take), and limits in computing power, all-atom molecular dynamics simulations have been severely limited in the timescales which they can study. While most proteins typically fold in the order of milliseconds,[14][16] before 2010, simulations could only reach nanosecond to microsecond timescales.[8] General-purpose supercomputers have been used to simulate protein folding, but such systems are intrinsically costly and typically shared among many research groups. Further, because the computations in kinetic models occur serially, strong scaling of traditional molecular simulations to these architectures is exceptionally difficult.[17][18] Moreover, as protein folding is a stochastic process and can statistically vary over time, it is challenging computationally to use long simulations for comprehensive views of the folding process.[19][20]

Protein folding does not occur in one step.[13] Instead, proteins spend most of their folding time, nearly 96% in some cases,[21] waiting in various intermediate conformational states, each a local thermodynamic free energy minimum in the protein's energy landscape. Through a process known as adaptive sampling, these conformations are used by Folding@home as starting points for a set of simulation trajectories. As the simulations discover more conformations, the trajectories are restarted from them, and a Markov state model (MSM) is gradually created from this cyclic process. MSMs are discrete-time master equation models which describe a biomolecule's conformational and energy landscape as a set of distinct structures and the short transitions between them. The adaptive sampling Markov state model method significantly increases the efficiency of simulation as it avoids computation inside the local energy minimum itself, and is amenable to distributed computing (including on GPUGRID) as it allows for the statistical aggregation of short, independent simulation trajectories.[22] The amount of time it takes to construct a Markov state model is inversely proportional to the number of parallel simulations run, i.e., the number of processors available. In other words, it achieves linear parallelization, leading to an approximately four orders of magnitude reduction in overall serial calculation time. A completed MSM may contain tens of thousands of sample states from the protein's phase space (all the conformations a protein can take on) and the transitions between them. The model illustrates folding events and pathways (i.e., routes) and researchers can later use kinetic clustering to view a coarse-grained representation of the otherwise highly detailed model. They can use these MSMs to reveal how proteins misfold and to quantitatively compare simulations with experiments.[5][19][23]

Between 2000 and 2010, the length of the proteins Folding@home has studied have increased by a factor of four, while its timescales for protein folding simulations have increased by six orders of magnitude.[24] In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months,[10] and in 2011, MSMs parallelized another simulation that required an aggregate 10million CPU hours of computing.[25] In January 2010, Folding@home used MSMs to simulate the dynamics of the slow-folding 32-residue NTL9 protein out to 1.52milliseconds, a timescale consistent with experimental folding rate predictions but a thousand times longer than formerly achieved. The model consisted of many individual trajectories, each two orders of magnitude shorter, and provided an unprecedented level of detail into the protein's energy landscape.[5][8][26] In 2010, Folding@home researcher Gregory Bowman was awarded the Thomas Kuhn Paradigm Shift Award from the American Chemical Society for the development of the open-source MSMBuilder software and for attaining quantitative agreement between theory and experiment.[27][28] For his work, Pande was awarded the 2012 Michael and Kate Brny Award for Young Investigators for "developing field-defining and field-changing computational methods to produce leading theoretical models for protein and RNA folding",[29] and the 2006 Irving Sigal Young Investigator Award for his simulation results which "have stimulated a re-examination of the meaning of both ensemble and single-molecule measurements, making Dr. Pande's efforts pioneering contributions to simulation methodology."[30]

Protein misfolding can result in a variety of diseases including Alzheimer's disease, cancer, CreutzfeldtJakob disease, cystic fibrosis, Huntington's disease, sickle-cell anemia, and typeII diabetes.[13][31][32] Cellular infection by viruses such as HIV and influenza also involve folding events on cell membranes.[33] Once protein misfolding is better understood, therapies can be developed that augment cells' natural ability to regulate protein folding. Such therapies include the use of engineered molecules to alter the production of a given protein, help destroy a misfolded protein, or assist in the folding process.[34] The combination of computational molecular modeling and experimental analysis has the possibility to fundamentally shape the future of molecular medicine and the rational design of therapeutics,[15] such as expediting and lowering the costs of drug discovery.[35] The goal of the first five years of Folding@home was to make advances in understanding folding, while the current goal is to understand misfolding and related disease, especially Alzheimer's.[36]

The simulations run on Folding@home are used in conjunction with laboratory experiments,[19] but researchers can use them to study how folding in vitro differs from folding in native cellular environments. This is advantageous in studying aspects of folding, misfolding, and their relationships to disease that are difficult to observe experimentally. For example, in 2011, Folding@home simulated protein folding inside a ribosomal exit tunnel, to help scientists better understand how natural confinement and crowding might influence the folding process.[37][38] Furthermore, scientists typically employ chemical denaturants to unfold proteins from their stable native state. It is not generally known how the denaturant affects the protein's refolding, and it is difficult to experimentally determine if these denatured states contain residual structures which may influence folding behavior. In 2010, Folding@home used GPUs to simulate the unfolded states of ProteinL, and predicted its collapse rate in strong agreement with experimental results.[39]

The Pande Lab is part of Stanford University, a non-profit entity, and does not sell the results generated by Folding@home.[40] The large data sets from the project are freely available for other researchers to use upon request and some can be accessed from the Folding@home website.[41][42] The Pande lab has collaborated with other molecular dynamics systems such as the Blue Gene supercomputer,[43] and they share Folding@home's key software with other researchers, so that the algorithms which benefited Folding@home may aid other scientific areas.[41] In 2011, they released the open-source Copernicus software, which is based on Folding@home's MSM and other parallelizing methods and aims to improve the efficiency and scaling of molecular simulations on large computer clusters or supercomputers.[44][45] Summaries of all scientific findings from Folding@home are posted on the Folding@home website after publication.[7]

Alzheimer's disease is linked to the aggregation of amyloid beta protein fragments in the brain (right). Researchers have used Folding@home to simulate this aggregation process, to better understand the cause of the disease.

Alzheimer's disease is an incurable neurodegenerative disease which most often affects the elderly and accounts for more than half of all cases of dementia. Its exact cause remains unknown, but the disease is identified as a protein misfolding disease. Alzheimer's is associated with toxic aggregations of the amyloid beta (A) peptide, caused by A misfolding and clumping together with other A peptides. These A aggregates then grow into significantly larger senile plaques, a pathological marker of Alzheimer's disease.[46][47][48] Due to the heterogeneous nature of these aggregates, experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) have had difficulty characterizing their structures. Moreover, atomic simulations of A aggregation are highly demanding computationally due to their size and complexity.[49][50]

Preventing A aggregation is a promising method to developing therapeutic drugs for Alzheimer's disease, according to Drs. Naeem and Fazili in a literature review article.[51] In 2008, Folding@home simulated the dynamics of A aggregation in atomic detail over timescales of the order of tens of seconds. Prior studies were only able to simulate about 10 microseconds. Folding@home was able to simulate A folding for six orders of magnitude longer than formerly possible. Researchers used the results of this study to identify a beta hairpin that was a major source of molecular interactions within the structure.[52] The study helped prepare the Pande lab for future aggregation studies and for further research to find a small peptide which may stabilize the aggregation process.[49]

In December 2008, Folding@home found several small drug candidates which appear to inhibit the toxicity of A aggregates.[53] In 2010, in close cooperation with the Center for Protein Folding Machinery, these drug leads began to be tested on biological tissue.[32] In 2011, Folding@home completed simulations of several mutations of A that appear to stabilize the aggregate formation, which could aid in the development of therapeutic drug therapies for the disease and greatly assist with experimental nuclear magnetic resonance spectroscopy studies of A oligomers.[50][54] Later that year, Folding@home began simulations of various A fragments to determine how various natural enzymes affect the structure and folding of A.[55][56]

Huntington's disease is a neurodegenerative genetic disorder that is associated with protein misfolding and aggregation. Excessive repeats of the glutamine amino acid at the N-terminus of the Huntingtin protein cause aggregation, and although the behavior of the repeats is not completely understood, it does lead to the cognitive decline associated with the disease.[57] As with other aggregates, there is difficulty in experimentally determining its structure.[58] Scientists are using Folding@home to study the structure of the Huntingtin protein aggregate and to predict how it forms, assisting with rational drug design methods to stop the aggregate formation.[32] The N17 fragment of the Huntington protein accelerates this aggregation, and while there have been several mechanisms proposed, its exact role in this process remains largely unknown.[59] Folding@home has simulated this and other fragments to clarify their roles in the disease.[60] Since 2008, its drug design methods for Alzheimer's disease have been applied to Huntington's.[32]

More than half of all known cancers involve mutations of p53, a tumor suppressor protein present in every cell which regulates the cell cycle and signals for cell death in the event of damage to DNA. Specific mutations in p53 can disrupt these functions, allowing an abnormal cell to continue growing unchecked, resulting in the development of tumors. Analysis of these mutations helps explain the root causes of p53-related cancers.[61] In 2004, Folding@home was used to perform the first molecular dynamics study of the refolding of p53's protein dimer in an all-atom simulation of water. The simulation's results agreed with experimental observations and gave insights into the refolding of the dimer that were formerly unobtainable.[62] This was the first peer reviewed publication on cancer from a distributed computing project.[63] The following year, Folding@home powered a new method to identify the amino acids crucial for the stability of a given protein, which was then used to study mutations of p53. The method was reasonably successful in identifying cancer-promoting mutations and determined the effects of specific mutations which could not otherwise be measured experimentally.[64]

Folding@home is also used to study protein chaperones,[32] heat shock proteins which play essential roles in cell survival by assisting with the folding of other proteins in the crowded and chemically stressful environment within a cell. Rapidly growing cancer cells rely on specific chaperones, and some chaperones play key roles in chemotherapy resistance. Inhibitions to these specific chaperones are seen as potential modes of action for efficient chemotherapy drugs or for reducing the spread of cancer.[65] Using Folding@home and working closely with the Center for Protein Folding Machinery, the Pande lab hopes to find a drug which inhibits those chaperones involved in cancerous cells.[66] Researchers are also using Folding@home to study other molecules related to cancer, such as the enzyme Src kinase, and some forms of the engrailed homeodomain: a large protein which may be involved in many diseases, including cancer.[67][68] In 2011, Folding@home began simulations of the dynamics of the small knottin protein EETI, which can identify carcinomas in imaging scans by binding to surface receptors of cancer cells.[69][70]

Interleukin 2 (IL-2) is a protein that helps T cells of the immune system attack pathogens and tumors. However, its use as a cancer treatment is restricted due to serious side effects such as pulmonary edema. IL-2 binds to these pulmonary cells differently than it does to T cells, so IL-2 research involves understanding the differences between these binding mechanisms. In 2012, Folding@home assisted with the discovery of a mutant form of IL-2 which is three hundred times more effective in its immune system role but carries fewer side effects. In experiments, this altered form significantly outperformed natural IL-2 in impeding tumor growth. Pharmaceutical companies have expressed interest in the mutant molecule, and the National Institutes of Health are testing it against a large variety of tumor models to try to accelerate its development as a therapeutic.[71][72]

Osteogenesis imperfecta, known as brittle bone disease, is an incurable genetic bone disorder which can be lethal. Those with the disease are unable to make functional connective bone tissue. This is most commonly due to a mutation in Type-I collagen,[73] which fulfills a variety of structural roles and is the most abundant protein in mammals.[74] The mutation causes a deformation in collagen's triple helix structure, which if not naturally destroyed, leads to abnormal and weakened bone tissue.[75] In 2005, Folding@home tested a new quantum mechanical method that improved upon prior simulation methods, and which may be useful for future computing studies of collagen.[76] Although researchers have used Folding@home to study collagen folding and misfolding, the interest stands as a pilot project compared to Alzheimer's and Huntington's research.[32]

Folding@home is assisting in research towards preventing some viruses, such as influenza and HIV, from recognizing and entering biological cells.[32] In 2011, Folding@home began simulations of the dynamics of the enzyme RNase H, a key component of HIV, to try to design drugs to deactivate it.[77] Folding@home has also been used to study membrane fusion, an essential event for viral infection and a wide range of biological functions. This fusion involves conformational changes of viral fusion proteins and protein docking,[33] but the exact molecular mechanisms behind fusion remain largely unknown.[78] Fusion events may consist of over a half million atoms interacting for hundreds of microseconds. This complexity limits typical computer simulations to about ten thousand atoms over tens of nanoseconds: a difference of several orders of magnitude.[52] The development of models to predict the mechanisms of membrane fusion will assist in the scientific understanding of how to target the process with antiviral drugs.[79] In 2006, scientists applied Markov state models and the Folding@home network to discover two pathways for fusion and gain other mechanistic insights.[52]

Following detailed simulations from Folding@home of small cells known as vesicles, in 2007, the Pande lab introduced a new computing method to measure the topology of its structural changes during fusion.[80] In 2009, researchers used Folding@home to study mutations of influenza hemagglutinin, a protein that attaches a virus to its host cell and assists with viral entry. Mutations to hemagglutinin affect how well the protein binds to a host's cell surface receptor molecules, which determines how infective the virus strain is to the host organism. Knowledge of the effects of hemagglutinin mutations assists in the development of antiviral drugs.[81][82] As of 2012, Folding@home continues to simulate the folding and interactions of hemagglutinin, complementing experimental studies at the University of Virginia.[32][83]

Drugs function by binding to specific locations on target molecules and causing some desired change, such as disabling a target or causing a conformational change. Ideally, a drug should act very specifically, and bind only to its target without interfering with other biological functions. However, it is difficult to precisely determine where and how tightly two molecules will bind. Due to limits in computing power, current in silico methods usually must trade speed for accuracy; e.g., use rapid protein docking methods instead of computationally costly free energy calculations. Folding@home's computing performance allows researchers to use both methods, and evaluate their efficiency and reliability.[36][84][85] Computer-assisted drug design has the potential to expedite and lower the costs of drug discovery.[35] In 2010, Folding@home used MSMs and free energy calculations to predict the native state of the villin protein to within 1.8 angstrom () root mean square deviation (RMSD) from the crystalline structure experimentally determined through X-ray crystallography. This accuracy has implications to future protein structure prediction methods, including for intrinsically unstructured proteins.[52] Scientists have used Folding@home to research drug resistance by studying vancomycin, an antibiotic drug of last resort, and beta-lactamase, a protein that can break down antibiotics like penicillin.[86][87]

Chemical activity occurs along a protein's active site. Traditional drug design methods involve tightly binding to this site and blocking its activity, under the assumption that the target protein exists in one rigid structure. However, this approach works for approximately only 15% of all proteins. Proteins contain allosteric sites which, when bound to by small molecules, can alter a protein's conformation and ultimately affect the protein's activity. These sites are attractive drug targets, but locating them is very computationally costly. In 2012, Folding@home and MSMs were used to identify allosteric sites in three medically relevant proteins: beta-lactamase, interleukin-2, and RNase H.[87][88]

Approximately half of all known antibiotics interfere with the workings of a bacteria's ribosome, a large and complex biochemical machine that performs protein biosynthesis by translating messenger RNA into proteins. Macrolide antibiotics clog the ribosome's exit tunnel, preventing synthesis of essential bacterial proteins. In 2007, the Pande lab received a grant to study and design new antibiotics.[32] In 2008, they used Folding@home to study the interior of this tunnel and how specific molecules may affect it.[89] The full structure of the ribosome was determined only as of 2011, and Folding@home has also simulated ribosomal proteins, as many of their functions remain largely unknown.[90]

There are many more protein misfolding promoted diseases that can be benefited from Folding@home to either discern the misfolded protein structure or the misfolding kinetics, and assist in drug design in the future. The often fatal prion diseases is among the most significant.

Prion (PrP) is a transmembrane cellular protein found widely in eukaryotic cells. In mammals, it is more abundant in the central nervous system. Although its function is unknown, its high conservation among species indicates an important role in the cellular function. The conformational change from the normal prion protein (PrPc, stands for cellular) to the disease causing isoform PrPSc (stands for prototypical prion diseasescrapie) causes a host of diseases collectly known as transmissible spongiform encephalopathies (TSEs), including Bovine spongiform encephalopathy (BSE) in bovine, Creutzfeldt-Jakob disease (CJD) and fatal insomnia in human, chronic wasting disease (CWD) in the deer family. The conformational change is widely accepted as the result of protein misfolding. What distinguishes TSEs from other protein misfolding diseases is its transmissible nature. The seeding of the infectious PrPSc, either arising spontaneously, hereditary or acquired via exposure to contaminated tissues,[91] can cause a chain reaction of transforming normal PrPc into fibrils aggregates or amyloid like plaques consist of PrPSc.[92]

The molecular structure of PrPSc has not been fully characterized due to its aggregated nature. Neither is known much about the mechanism of the protein misfolding nor its kinetics. Using the known structure of PrPc and the results of the in vitro and in vivo studies described below, Folding@home could be valuable in elucidating how PrPSc is formed and how the infectious protein arrange themselves to form fibrils and amyloid like plaques, bypassing the requirement to purify PrPSc or dissolve the aggregates.

The PrPc has been enzymatically dissociated from the membrane and purified, its structure studied using structure characterization techniques such as NMR spectroscopy and X-ray crystallography. Post-translational PrPc has 231 amino acids (aa) in murine. The molecule consists of a long and unstructured amino terminal region spanning up to aa residue 121 and a structured carboxy terminal domain.[92] This globular domain harbours two short sheet-forming anti-parallel -strands (aa 128 to 130 and aa 160 to 162 in murine PrPc) and three -helices (helix I: aa 143 to 153; helix II: aa 171 to 192; helix III: aa 199 to 226 in murine PrPc),[93] Helices II and III are anti-parallel orientated and connected by a short loop. Their structural stability is supported by a disulfide bridge, which is parallel to both sheet-forming -strands. These -helices and the -sheet form the rigid core of the globular domain of PrPc.[94]

The disease causing PrPSc is proteinase K resistant and insoluble. Attempts to purify it from the brains of infected animals invariably yield heterogeneous mixtures and aggregated states that are not amenable to characterization by NMR spectroscopy or X-ray crystallography. However, it is a general consensus that PrPSc contains a high percentage of tightly stacked -sheets than the normal PrPc that renders the protein insoluble and resistant to proteinase. Using techniques of cryoelectron microscopy and structural modeling based on similar common protein structures, it has been discovered that PrPSc contains -sheets in the region of aa 81-95 to aa 171, while the carboxy terminal structure is supposedly preserved, retaining the disulfide-linked -helical conformation in the normal PrPc. These -sheets form a parallel left-handed beta-helix.[92] Three PrPSc molecules are believed to form a primary unit and therefore build the basis for the so-called scrapie-associated fibrils.[95] The catalytic activity depends on the size of the particle. PrPSc particles which consist of only 14-28 PrPc molecules exhibit the highest rate of infectivity and conversion.[96]

Despite the difficulty to purify and characterize PrPSc, from the known molecular structure of PrPc and using transgenic mice and N-terminal deletion,[97] the potential hot spots of protein misfolding leading to the pathogenic PrPSc could be deduced and Folding@home could be of great value in confirming these. Studies found that both the primary and secondary structure of the prion protein can be of significance of the conversion.

There are more than twenty mutations of the prion protein gene (PRNP) that are known to be associated with or that are directly linked to the hereditary form of human TSEs [56], indicating single amino acids at certain position, likely within the carboxy domain,[93] of the PrPc can affect the susceptibility to TSEs.

The post-translational amino terminal region of PrPc consists of residues 23-120 which make up nearly half of the amino sequence of full-length matured PrPc. There are two sections in the amino terminal region that may influence conversion. First, residues 52-90 contains an octapeptide repeat (5 times) region that likely influences the initial binding (via the octapeptide repeats) and also the actual conversion via the second section of aa 108-124.[98] The highly hydrophobic AGAAAAGA is located between aa residue 113 and 120 and is described as putative aggregation site,[99] although this sequence requires its flanking parts to form fibrillar aggregates.[100]

In the carboxy globular domain,[94] among the three helices, study show that helix II has a significant higher propensity to -strand conformation.[101] Due to the high conformational flexvoribility seen between residues 114-125 (part of the unstructured N-terminus chain) and the high -strand propensity of helix II, only moderate changes in the environmental conditions or interactions might be sufficient to induce misfolding of PrPc and subsequent fibril formation.[92]

Other studies of NMR structures of PrPc showed that these residues (~108189) contain most of the folded domain including both -strands, the first two -helices, and the loop/turn regions connecting them, but not the helix III.[97] Small changes within the loop/turn structures of PrPc itself could be important in the conversion as well.[102] In another study, Riek et al. showed that the two small regions of -strand upstream of the loop regions act as a nucleation site for the conformational conversion of the loop/turn and -helical structures in PrPc to -sheet.[93]

The energy threshold for the conversion are not necessarily high. The folding stability, i.e. the free energy of a globular protein in its environment is in the range of one or two hydrogen bonds thus allows the transition to an isoform without the requirement of high transition energy.[92]

From the respective of the interactions among the PrPc molecules, hydrophobic interactions play a crucial role in the formation of -sheets, a hallmark of PrPSc, as the sheets bring fragments of polypeptide chains into close proximity.[103] Indeed, Kutznetsov and Rackovsky [104] showed that disease-promoting mutations in the human PrPc had a statistically significant tendency towards increasing local hydrophobicity.

In vitro experiments showed the kinetics of misfolding has an initial lag phase followed by a rapid growth phase of fibril formation.[105] It is likely that PrPc goes through some intermediate states, such as at least partially unfolded or degraded, before finally ending up as part of an amyloid fibril.[92]

This section needs to be updated. Please update this article to reflect recent events or newly available information. (June 2016)

Like other distributed computing projects, Folding@home is an online citizen science project. In these projects non-specialists contribute computer processing power or help to analyse data produced by professional scientists. Participants in these projects play an invaluable role in facilitating research for little or no obvious reward.

Research has been carried out into the motivations of citizen scientists and most of these studies have found that participants are motivated to take part because of altruistic reasons, that is, they want to help scientists and make a contribution to the advancement of their research.[106][107][108][109] Many participants in citizen science have an underlying interest in the topic of the research and gravitate towards projects that are in disciplines of interest to them. Folding@home is no different in that respect.[110] Research carried out recently on over 400 active participants revealed that they wanted to help make a contribution to research and that many had friends or relatives affected by the diseases that the Folding@home scientists investigate.

Folding@home attracts participants who are computer hardware enthusiasts (sometimes called overclockers). These groups bring considerable expertise to the project and are able to build computers with advanced processing power.[111] Other distributed computing projects attract these types of participants and projects are often used to benchmark the performance of modified computers, and this aspect of the hobby is accommodated through the competitive nature of the project. Individuals and teams can compete to see who can process the most computer processing units (CPUs).

This latest research on Folding@home involving interview and ethnographic observation of online groups showed that teams of hardware enthusiasts can sometimes work together, sharing best practice with regard to maximising processing output. Such teams can become communities of practice, with a shared language and online culture. This pattern of participation has been observed in other distributed computing projects.[112][113]

Another key observation of Folding@home participants is that many are male.[110] This has also been observed in other distributed projects. Furthermore, many participants work in computer and technology-based jobs and careers.[110][114][115]

Not all Folding@home participants are hardware enthusiasts. Many participants run the project software on unmodified machines and do take part competitively. Over 100,000 participants are involved in Folding@home. However, it is difficult to ascertain what proportion of participants are hardware enthusiasts. Although, according to the project managers, the contribution of the enthusiast community is substantially larger in terms of processing power.[116]

On September 16, 2007, due in large part to the participation of PlayStation 3 consoles, the Folding@home project officially attained a sustained performance level higher than one native petaFLOPS, becoming the first computing system of any kind to do so.[122][123] Top500's fastest supercomputer at the time was BlueGene/L, at 0.280 petaFLOPS.[124] The following year, on May 7, 2008, the project attained a sustained performance level higher than two native petaFLOPS,[125] followed by the three and four native petaFLOPS milestones on August 2008[126][127] and September 28, 2008 respectively.[128] On February 18, 2009, Folding@home achieved five native petaFLOPS,[129][130] and was the first computing project to meet these five levels.[132] In comparison, November 2008's fastest supercomputer was IBM's Roadrunner at 1.105 petaFLOPS.[133] On November 10, 2011, Folding@home's performance exceeded six native petaFLOPS with the equivalent of nearly eight x86 petaFLOPS.[123][134] In mid-May 2013, Folding@home attained over seven native petaFLOPS, with the equivalent of 14.87 x86 petaFLOPS. It then reached eight native petaFLOPS on June 21, followed by nine on September 9 of that year, with 17.9 x86 petaFLOPS.[135] On May 11, 2016 Folding@home announced that it was moving towards reaching the 100 x86 petaFLOPS mark.[136]

Similarly to other distributed computing projects, Folding@home quantitatively assesses user computing contributions to the project through a credit system.[137] All units from a given protein project have uniform base credit, which is determined by benchmarking one or more work units from that project on an official reference machine before the project is released.[137] Each user receives these base points for completing every work unit, though through the use of a passkey they can receive added bonus points for reliably and rapidly completing units which are more demanding computationally or have a greater scientific priority.[138][139] Users may also receive credit for their work by clients on multiple machines.[40] This point system attempts to align awarded credit with the value of the scientific results.[137]

Users can register their contributions under a team, which combine the points of all their members. A user can start their own team, or they can join an existing team.[140] In some cases, a team may have their own community-driven sources of help or recruitment such as an Internet forum.[141] The points can foster friendly competition between individuals and teams to compute the most for the project, which can benefit the folding community and accelerate scientific research.[137][142][143] Individual and team statistics are posted on the Folding@home website.[137]

If a user does not form a new team, or does not join an existing team, that user automatically becomes part of a "Default" team. This "Default" team has a team number of "0". Statistics are accumulated for this "Default" team as well as for specially named teams.

Folding@home software at the user's end involves three primary components: work units, cores, and a client.

A work unit is the protein data that the client is asked to process. Work units are a fraction of the simulation between the states in a Markov state model. After the work unit has been downloaded and completely processed by a volunteer's computer, it is returned to Folding@home servers, which then award the volunteer the credit points. This cycle repeats automatically.[142] All work units have associated deadlines, and if this deadline is exceeded, the user may not get credit and the unit will be automatically reissued to another participant. As protein folding occurs serially, and many work units are generated from their predecessors, this allows the overall simulation process to proceed normally if a work unit is not returned after a reasonable period of time. Due to these deadlines, the minimum system requirement for Folding@home is a Pentium3 450MHz CPU with Streaming SIMD Extensions (SSE).[40] However, work units for high-performance clients have a much shorter deadline than those for the uniprocessor client, as a major part of the scientific benefit is dependent on rapidly completing simulations.[144]

Before public release, work units go through several quality assurance steps to keep problematic ones from becoming fully available. These testing stages include internal, beta, and advanced, before a final full release across Folding@home.[145] Folding@home's work units are normally processed only once, except in the rare event that errors occur during processing. If this occurs for three different users, the unit is automatically pulled from distribution.[146][147] The Folding@home support forum can be used to differentiate between issues arising from problematic hardware and bad work units.[148]

Specialized molecular dynamics programs, referred to as "FahCores" and often abbreviated "cores", perform the calculations on the work unit as a background process. A large majority of Folding@home's cores are based on GROMACS,[142] one of the fastest and most popular molecular dynamics software packages, which largely consists of manually optimized assembly language code and hardware optimizations.[149][150] Although GROMACS is open-source software and there is a cooperative effort between the Pande lab and GROMACS developers, Folding@home uses a closed-source license to help ensure data validity.[151] Less active cores include ProtoMol and SHARPEN. Folding@home has used AMBER, CPMD, Desmond, and TINKER, but these have since been retired and are no longer in active service.[3][152][153] Some of these cores perform explicit solvation calculations in which the surrounding solvent (usually water) is modeled atom-by-atom; while others perform implicit solvation methods, where the solvent is treated as a mathematical continuum.[154][155] The core is separate from the client to enable the scientific methods to be updated automatically without requiring a client update. The cores periodically create calculation checkpoints so that if they are interrupted they can resume work from that point upon startup.[142]

A Folding@home participant installs a client program on their personal computer. The user interacts with the client, which manages the other software components in the background. Through the client, the user may pause the folding process, open an event log, check the work progress, or view personal statistics.[156] The computer clients run continuously in the background at a very low priority, using idle processing power so that normal computer use is unaffected.[40][140] The maximum CPU use can be adjusted via client settings.[156][157] The client connects to a Folding@home server and retrieves a work unit and may also download the appropriate core for the client's settings, operating system, and the underlying hardware architecture. After processing, the work unit is returned to the Folding@home servers. Computer clients are tailored to uniprocessor and multi-core processor systems, and graphics processing units. The diversity and power of each hardware architecture provides Folding@home with the ability to efficiently complete many types of simulations in a timely manner (in a few weeks or months rather than years), which is of significant scientific value. Together, these clients allow researchers to study biomedical questions formerly considered impractical to tackle computationally.[36][142][144]

Professional software developers are responsible for most of Folding@home's code, both for the client and server-side. The development team includes programmers from Nvidia, ATI, Sony, and Cauldron Development.[158] Clients can be downloaded only from the official Folding@home website or its commercial partners, and will only interact with Folding@home computer files. They will upload and download data with Folding@home's data servers (over port8080, with 80 as an alternate), and the communication is verified using 2048-bit digital signatures.[40][159] While the client's graphical user interface (GUI) is open-source,[160] the client is proprietary software citing security and scientific integrity as the reasons.[161][162][163]

However, this rationale of using proprietary software is disputed since while the license could be enforceable in the legal domain retrospectively, it doesn't practically prevent the modification (also known as patching) of the executable binary files. Likewise, binary-only distribution does not prevent the malicious modification of executable binary-code, either through a man-in-the-middle attack while being downloaded via the internet,[164] or by the redistribution of binaries by a third-party that have been previously modified either in their binary state (i.e. patched),[165] or by decompiling[166] and recompiling them after modification.[167][168] Unless the binary files and the transport channel are signed and the recipient person/system is able to verify the digital signature, in which case unwarranted modifications should be detectable, but not always.[169] Either way, since in the case of Folding@Home the input data and output result processed by the client-software are both digitally signed,[40][159] the integrity of work can be verified independently from the integrity of the client software itself.

Folding@home uses the Cosm software libraries for networking.[142][158] Folding@home was launched on October1, 2000, and was the first distributed computing project aimed at bio-molecular systems.[170] Its first client was a screensaver, which would run while the computer was not otherwise in use.[171][172] In 2004, the Pande lab collaborated with David P. Anderson to test a supplemental client on the open-source BOINC framework. This client was released to closed beta in April 2005;[173] however, the method became unworkable and was shelved in June 2006.[174]

The specialized hardware of graphics processing units (GPU) is designed to accelerate rendering of 3-Dgraphics applications such as video games and can significantly outperform CPUs for some types of calculations. GPUs are one of the most powerful and rapidly growing computing platforms, and many scientists and researchers are pursuing general-purpose computing on graphics processing units (GPGPU). However, GPU hardware is difficult to use for non-graphics tasks and usually requires significant algorithm restructuring and an advanced understanding of the underlying architecture.[175] Such customization is challenging, more so to researchers with limited software development resources. Folding@home uses the open-source OpenMM library, which uses a bridge design pattern with two application programming interface (API) levels to interface molecular simulation software to an underlying hardware architecture. With the addition of hardware optimizations, OpenMM-based GPU simulations need no significant modification but achieve performance nearly equal to hand-tuned GPU code, and greatly outperform CPU implementations.[154][176]

Before 2010, the computing reliability of GPGPU consumer-grade hardware was largely unknown, and circumstantial evidence related to the lack of built-in error detection and correction in GPU memory raised reliability concerns. In the first large-scale test of GPU scientific accuracy, a 2010 study of over 20,000 hosts on the Folding@home network detected soft errors in the memory subsystems of two-thirds of the tested GPUs. These errors strongly correlated to board architecture, though the study concluded that reliable GPU computing was very feasible as long as attention is paid to the hardware traits, such as software-side error detection.[177]

The first generation of Folding@home's GPU client (GPU1) was released to the public on October2, 2006,[174] delivering a 2030 times speedup for some calculations over its CPU-based GROMACS counterparts.[178] It was the first time GPUs had been used for either distributed computing or major molecular dynamics calculations.[179][180] GPU1 gave researchers significant knowledge and experience with the development of GPGPU software, but in response to scientific inaccuracies with DirectX, on April10, 2008 it was succeeded by GPU2, the second generation of the client.[178][181] Following the introduction of GPU2, GPU1 was officially retired on June6.[178] Compared to GPU1, GPU2 was more scientifically reliable and productive, ran on ATI and CUDA-enabled Nvidia GPUs, and supported more advanced algorithms, larger proteins, and real-time visualization of the protein simulation.[182][183] Following this, the third generation of Folding@home's GPU client (GPU3) was released on May25, 2010. While backward compatible with GPU2, GPU3 was more stable, efficient, and flexibile in its scientific abilities,[184] and used OpenMM on top of an OpenCL framework.[184][185] Although these GPU3 clients did not natively support the operating systems Linux and macOS, Linux users with Nvidia graphics cards were able to run them through the Wine software application.[186][187] GPUs remain Folding@home's most powerful platform in FLOPS. As of November 2012, GPU clients account for 87% of the entire project's x86 FLOPS throughput.[188]

Native support for Nvidia and AMD graphics cards under Linux was introduced with FahCore 17, which uses OpenCL rather than CUDA.[189]

From March 2007 until November 2012, Folding@home took advantage of the computing power of PlayStation 3s. At the time of its inception, its main streaming Cell processor delivered a 20 times speed increase over PCs for some calculations, processing power which could not be found on other systems such as the Xbox 360.[36][190] The PS3's high speed and efficiency introduced other opportunities for worthwhile optimizations according to Amdahl's law, and significantly changed the tradeoff between computing efficiency and overall accuracy, allowing the use of more complex molecular models at little added computing cost.[191] This allowed Folding@home to run biomedical calculations that would have been otherwise infeasible computationally.[192]

The PS3 client was developed in a collaborative effort between Sony and the Pande lab and was first released as a standalone client on March23, 2007.[36][193] Its release made Folding@home the first distributed computing project to use PS3s.[194] On September18 of the following year, the PS3 client became a channel of Life with PlayStation on its launch.[195][196] In the types of calculations it can perform, at the time of its introduction, the client fit in between a CPU's flexibility and a GPU's speed.[142] However, unlike clients running on personal computers, users were unable to perform other activities on their PS3 while running Folding@home.[192] The PS3's uniform console environment made technical support easier and made Folding@home more user friendly.[36] The PS3 also had the ability to stream data quickly to its GPU, which was used for real-time atomic-level visualizing of the current protein dynamics.[191]

On November 6, 2012, Sony ended support for the Folding@home PS3 client and other services available under Life with PlayStation. Over its lifetime of five years and seven months, more than 15 million users contributed over 100 million hours of computing to Folding@home, greatly assisting the project with disease research. Following discussions with the Pande lab, Sony decided to terminate the application. Pande considered the PlayStation 3 client a "game changer" for the project.[197][198][199]

Folding@home can use the parallel computing abilities of modern multi-core processors. The ability to use several CPU cores simultaneously allows completing the full simulation far faster. Working together, these CPU cores complete single work units proportionately faster than the standard uniprocessor client. This method is scientifically valuable because it enables much longer simulation trajectories to be performed in the same amount of time, and reduces the traditional difficulties of scaling a large simulation to many separate processors.[200] A 2007 publication in the Journal of Molecular Biology relied on multi-core processing to simulate the folding of part of the villin protein approximately 10 times longer than was possible with a single-processor client, in agreement with experimental folding rates.[201]

In November 2006, first-generation symmetric multiprocessing (SMP) clients were publicly released for open beta testing, referred to as SMP1.[174] These clients used Message Passing Interface (MPI) communication protocols for parallel processing, as at that time the GROMACS cores were not designed to be used with multiple threads.[144] This was the first time a distributed computing project had used MPI.[202] Although the clients performed well in Unix-based operating systems such as Linux and macOS, they were troublesome under Windows.[200][202] On January24, 2010, SMP2, the second generation of the SMP clients and the successor to SMP1, was released as an open beta and replaced the complex MPI with a more reliable thread-based implementation.[139][158]

SMP2 supports a trial of a special category of bigadv work units, designed to simulate proteins that are unusually large and computationally intensive and have a great scientific priority. These units originally required a minimum of eight CPU cores,[203] which was raised to sixteen later, on February7, 2012.[204] Along with these added hardware requirements over standard SMP2 work units, they require more system resources such as random-access memory (RAM) and Internet bandwidth. In return, users who run these are rewarded with a 20% increase over SMP2's bonus point system.[205] The bigadv category allows Folding@home to run especially demanding simulations for long times that had formerly required use of supercomputing clusters and could not be performed anywhere else on Folding@home.[203] Many users with hardware able to run bigadv units have later had their hardware setup deemed ineligible for bigadv work units when CPU core minimums were increased, leaving them only able to run the normal SMP work units. This frustrated many users who invested significant amounts of money into the program only to have their hardware be obsolete for bigadv purposes shortly after. As a result, Pande announced in January 2014 that the bigadv program would end on January 31, 2015.[206]

The V7 client is the seventh and latest generation of the Folding@home client software, and is a full rewrite and unification of the prior clients for Windows, macOS, and Linux operating systems.[207][208] It was released on March22, 2012.[209] Like its predecessors, V7 can run Folding@home in the background at a very low priority, allowing other applications to use CPU resources as they need. It is designed to make the installation, start-up, and operation more user-friendly for novices, and offer greater scientific flexibility to researchers than prior clients.[210] V7 uses Trac for managing its bug tickets so that users can see its development process and provide feedback.[208]

V7 consists of four integrated elements. The user typically interacts with V7's open-source GUI, named FAHControl.[160][211] This has Novice, Advanced, and Expert user interface modes, and has the ability to monitor, configure, and control many remote folding clients from one computer. FAHControl directs FAHClient, a back-end application that in turn manages each FAHSlot (or slot). Each slot acts as replacement for the formerly distinct Folding@home v6 uniprocessor, SMP, or GPU computer clients, as it can download, process, and upload work units independently. The FAHViewer function, modeled after the PS3's viewer, displays a real-time 3-D rendering, if available, of the protein currently being processed.[207][208]

In 2014, a client for the Google Chrome and Chromium web browsers was released, allowing users to run Folding@home in their web browser. The client uses Google's Native Client (NaCl) feature on Chromium-based web browsers to run the Folding@Home code at near-native speed in a sandbox on the user's machine.[212] Due to the phasing out of NaCL and changes at Folding@Home, the web client was permanently shut down in June 2019.[213]

In July 2015, a client for Android mobile phones was released on Google Play for devices running Android 4.4 KitKat or newer.[214][215]

On the 16th of February 2018 the android client, which was offered in cooperation with Sony, was removed from the Google Play. Plans were announced to offer an open source alternative in the future.[216]

Rosetta@home is a distributed computing project aimed at protein structure prediction and is one of the most accurate tertiary structure predictors.[217][218] The conformational states from Rosetta's software can be used to initialize a Markov state model as starting points for Folding@home simulations.[22] Conversely, structure prediction algorithms can be improved from thermodynamic and kinetic models and the sampling aspects of protein folding simulations.[219] As Rosetta only tries to predict the final folded state, and not how folding proceeds, Rosetta@home and Folding@home are complementary and address very different molecular questions.[22][220]

Anton is a special-purpose supercomputer built for molecular dynamics simulations. In October 2011, Anton and Folding@home were the two most powerful molecular dynamics systems.[221] Anton is unique in its ability to produce single ultra-long computationally costly molecular trajectories,[222] such as one in 2010 which reached the millisecond range.[223][224] These long trajectories may be especially helpful for some types of biochemical problems.[225][226] However, Anton does not use Markov state models (MSM) for analysis. In 2011, the Pande lab constructed a MSM from two 100-s Anton simulations and found alternative folding pathways that were not visible through Anton's traditional analysis. They concluded that there was little difference between MSMs constructed from a limited number of long trajectories or one assembled from many shorter trajectories.[222] In June 2011 Folding@home began added sampling of an Anton simulation in an effort to better determine how its methods compare to Anton's.[227][228] However, unlike Folding@home's shorter trajectories, which are more amenable to distributed computing and other parallelizing methods, longer trajectories do not require adaptive sampling to sufficiently sample the protein's phase space. Due to this, it is possible that a combination of Anton's and Folding@home's simulation methods would provide a more thorough sampling of this space.[222]

Excerpt from:

Folding@home - Wikipedia

In medicine, proteopathy (Proteo- [pref. protein]; -pathy [suff. disease]; proteopathies pl.; proteopathic adj.) refers to a class of diseases in which certain proteins become structurally abnormal, and thereby disrupt the function of cells, tissues and organs of the body.[1][2] Often the proteins fail to fold into their normal configuration; in this misfolded state, the proteins can become toxic in some way (a gain of toxic function) or they can lose their normal function.[3] The proteopathies (also known as proteinopathies, protein conformational disorders, or protein misfolding diseases) include such diseases as CreutzfeldtJakob disease and other prion diseases, Alzheimer's disease, Parkinson's disease, amyloidosis, Multiple System Atrophy, and a wide range of other disorders (see List of Proteopathies).[2][4][5][6][7][8]

The concept of proteopathy can trace its origins to the mid-19th century, when, in 1854, Rudolf Virchow coined the term amyloid ("starch-like") to describe a substance in cerebral corpora amylacea that exhibited a chemical reaction resembling that of cellulose. In 1859, Friedreich and Kekul demonstrated that, rather than consisting of cellulose, "amyloid" actually is rich in protein.[9] Subsequent research has shown that many different proteins can form amyloid, and that all amyloids have in common birefringence in cross-polarized light after staining with the dye Congo Red, as well as a fibrillar ultrastructure when viewed with an electron microscope.[9] However, some proteinaceous lesions lack birefringence and contain few or no classical amyloid fibrils, such as the diffuse deposits of A protein in the brains of Alzheimer patients.[10] Furthermore, evidence has emerged that small, non-fibrillar protein aggregates known as oligomers are toxic to the cells of an affected organ, and that amyloidogenic proteins in their fibrillar form may be relatively benign.[11][12]

In most, if not all proteopathies, a change in 3-dimensional folding (conformation) increases the tendency of a specific protein to bind to itself.[5] In this aggregated form, the protein is resistant to clearance and can interfere with the normal capacity of the affected organs. In some cases, misfolding of the protein results in a loss of its usual function. For example, cystic fibrosis is caused by a defective cystic fibrosis transmembrane conductance regulator (CFTR) protein,[3] and in amyotrophic lateral sclerosis / frontotemporal lobar degeneration (FTLD), certain gene-regulating proteins inappropriately aggregate in the cytoplasm, and thus are unable to perform their normal tasks within the nucleus.[13][14] Because proteins share a common structural feature known as the polypeptide backbone, all proteins have the potential to misfold under some circumstances.[15] However, only a relatively small number of proteins are linked to proteopathic disorders, possibly due to structural idiosyncrasies of the vulnerable proteins. For example, proteins that are normally unfolded or relatively unstable as monomers (that is, as single, unbound protein molecules) are more likely to misfold into an abnormal conformation.[5][15][16] In nearly all instances, the disease-causing molecular configuration involves an increase in beta-sheet secondary structure of the protein.[5][15][17][18] The abnormal proteins in some proteopathies have been shown to fold into multiple 3-dimensional shapes; these variant, proteinaceous structures are defined by their different pathogenic, biochemical, and conformational properties.[19] They have been most thoroughly studied with regard to prion disease, and are referred to as protein strains.[20][21]

The likelihood that proteopathy will develop is increased by certain risk factors that promote the self-assembly of a protein. These include destabilizing changes in the primary amino acid sequence of the protein, post-translational modifications (such as hyperphosphorylation), changes in temperature or pH, an increase in production of a protein, or a decrease in its clearance.[1][5][15] Advancing age is a strong risk factor,[1] as is traumatic brain injury.[22][23] In the aging brain, multiple proteopathies can overlap.[24] For example, in addition to tauopathy and A-amyloidosis (which coexist as key pathologic features of Alzheimer's disease), many Alzheimer patients have concomitant synucleinopathy (Lewy bodies) in the brain.[25]

It is hypothesized that chaperones and co-chaperones (proteins that assist protein folding) may antagonize proteotoxicity during aging and in protein misfolding-diseases to maintain proteostasis.[26][27][28]

Some proteins can be induced to form abnormal assemblies by exposure to the same (or similar) protein that has folded into a disease-causing conformation, a process called 'seeding' or 'permissive templating'.[29][30] In this way, the disease state can be brought about in a susceptible host by the introduction of diseased tissue extract from an afflicted donor. The best known form of such inducible proteopathy is prion disease,[31] which can be transmitted by exposure of a host organism to purified prion protein in a disease-causing conformation.[32][33] There is now evidence that other proteopathies can be induced by a similar mechanism, including A amyloidosis, amyloid A (AA) amyloidosis, and apolipoprotein AII amyloidosis,[30][34] tauopathy,[35] synucleinopathy,[36][37][38][39] and the aggregation of superoxide dismutase-1 (SOD1),[40][41] polyglutamine,[42][43] and TAR DNA-binding protein-43 (TDP-43).[44]

In all of these instances, an aberrant form of the protein itself appears to be the pathogenic agent. In some cases, the deposition of one type of protein can be experimentally induced by aggregated assemblies of other proteins that are rich in -sheet structure, possibly because of structural complementarity of the protein molecules. For example, AA amyloidosis can be stimulated in mice by such diverse macromolecules as silk, the yeast amyloid Sup35, and curli fibrils from the bacterium Escherichia coli.[45] In addition, apolipoprotein AII amyloid can be induced in mice by a variety of -sheet rich amyloid fibrils,[46] and cerebral tauopathy can be induced by brain extracts that are rich in aggregated A.[47] There is also experimental evidence for cross-seeding between prion protein and A.[48] In general, such heterologous seeding is less efficient than is seeding by a corrupted form of the same protein.

The development of effective treatments for many proteopathies has been challenging.[73][74] Because the proteopathies often involve different proteins arising from different sources, treatment strategies must be customized to each disorder; however, general therapeutic approaches include maintaining the function of affected organs, reducing the formation of the disease-causing proteins, preventing the proteins from misfolding and/or aggregating, or promoting their removal.[75][73][76] For example, in Alzheimer's disease, researchers are seeking ways to reduce the production of the disease-associated protein A by inhibiting the enzymes that free it from its parent protein.[74] Another strategy is to use antibodies to neutralize specific proteins by active or passive immunization.[77] In some proteopathies, inhibiting the toxic effects of protein oligomers might be beneficial.[78] Amyloid A (AA) amyloidosis can be reduced by treating the inflammatory state that increases the amount of the protein in the blood (referred to as serum amyloid A, or SAA).[73] In immunoglobulin light chain amyloidosis (AL amyloidosis), chemotherapy can be used to lower the number of the blood cells that make the light chain protein that forms amyloid in various bodily organs.[79] Transthyretin (TTR) amyloidosis (ATTR) results from the deposition of misfolded TTR in multiple organs.[80] Because TTR is mainly produced in the liver, TTR amyloidosis can be slowed in some hereditary cases by liver transplantation.[81] TTR amyloidosis also can be treated by stabilizing the normal assemblies of the protein (called tetramers because they consist of four TTR molecules bound together). Stabilization prevents individual TTR molecules from escaping, misfolding, and aggregating into amyloid.[82][83]

Several other treatment strategies for proteopathies are being investigated, including small molecules and biologic medicines such as small interfering RNAs, antisense oligonucleotides, peptides, and engineered immune cells.[82][79][84][85] In some cases, multiple therapeutic agents may be combined to improve effectiveness.[79][86]

Micrograph of tauopathy (brown) in a neuronal cell body (arrow) and process (arrowhead) in the cerebral cortex of a patient with Alzheimer's disease. Bar = 25 microns (0.025mm).

View post:

Proteopathy - Wikipedia

Protein folding is a process in which a polypeptide folds into a specific, stable, functional, three-dimensional structure. It is the process by which a protein structure assumes its functional shape or conformation

Proteins are formed from long chains of amino acids; they exist in an array of different structures which often dictate their functions. Proteins follow energetically favorable pathways to form stable, orderly, structures; this is known as the proteins native structure. Most proteins can only perform their various functions when they are folded. The proteins folding pathway, or mechanism, is the typical sequence of structural changes the protein undergoes in order to reach its native structure. Protein folding takes place in a highly crowded, complex, molecular environment within the cell, and often requires the assistance of molecular chaperones, in order to avoid aggregation or misfolding. Proteins are comprised of amino acids with various types of side chains, which may be hydrophobic, hydrophilic, or electrically charged. The characteristics of these side chains affect what shape the protein will form because they will interact differently intramolecularly and with the surrounding environment, favoring certain conformations and structures over others. Scientists believe that the instructions for folding a protein are encoded in the sequence. Researchers and scientists can easily determine the sequence of a protein, but have not cracked the code that governs folding (Structures of Life 8).

Early scientists who studied proteomics and its structure speculated that proteins had templates that resulted in their native conformations. This theory resulted in a search for how proteins fold to attain their complex structure. It is now well known that under physiological conditions, proteins normally spontaneously fold into their native conformations. As a result, a protein's primary structure is valuable since it determines the three-dimensional structure of a protein. Normally, most biological structures do not have the need for external templates to help with their formation and are thus called self-assembling.

Protein renaturation known since the 1930s. However, it was not until 1957 when Christian Anfinsen performed an experiment on bovine pancreatic RNase A that protein renaturation was quantified. RNase A is a single chain protein consisting of 124 residues. In 8M urea solution of 2-mercaptoethanol, the RNase A is completely unfolded and has its four disulfide bonds cleaved through reduction. Through dialysis of urea and introducing the solution to O2 at pH 8, the enzymatically active protein is physically incapable of being recognized from RNase A. As a result, this experiment demonstrated that the protein spontaneously renatured.

One criteria for the renaturation of RNase A is for its four disulfide bonds to reform. The likelihood of one of the eight Cys residues from RNase A reforming a disulfide bond with its native residue compared to the other seven Cys residues is 1/7. Furthermore, the next one of remaining six Cys residues randomly forming the next disulfide bond is 1/5 and etc. As a result, the probability of RNase A reforming four native disulfide links at random is (1/7 * 1/5 * 1/3 * 1/1 = 1/105). The result of this probability demonstrates that forming the disulfide bonds from RNase A is not a random activity.

When RNase A is reoxidized utilizing 8M urea, allowing the disulfide bonds to reform when the polypeptide chain is a random coil, then RNase A will only be around 1 percent enzymatically active after urea is removed. However, by using 2-mercaptoethanol, the protein can be made fully active once again when disulfide bond interchange reactions occur and the protein is back to its native state. The native state of the RNase A is thermodynamically stable under physiological conditions, especially since a more stable protein that is more stable than that of the native state requires a larger activation barrier, and is kinetically inaccessible.

By using the enzyme protein disulfide isomerase (PDI), the time it takes for randomized RNase A is minimized to about 2 minutes. This enzyme helps facilitate the disulfide interchange reactions. In order for PDI to be active, its two active site Cys residues needs to be in the -SH form. Furthermore, PDI helps with random cleavage and the reformation of the disulfide bonds of the protein as it attain thermodynamically favorable conformations.

Proteins in a "scrambled" state go through PDI to renature, and their native state does not utilize PDI because native proteins are in their stable conformations. However, proteins that are posttranslationally modified need the disulfide bonds to stabilize their rather unstable native form. One example of this is insulin, a polypeptide hormone. This 51 residue polypeptide has two disulfide bonds that is inactivated by PDI. The following link is an image showing insulin with its two disulfide bonds. Through observation of this phenomenon, scientists were able to find that insulin is made from proinsulin, an 84-residue single chain. This link provides more information on the structure of proinsulin and its progression on becoming insulin. The disulfide bonds of proinsulin need to be intact before conversion of becoming insulin through proteolytic excision of its C chain which is an internal 33-residue segment. However, according to two findings, the C chain is not what dictates the folding of the A and B chains, but instead holds them together to allow formation of the disulfide bonds. For one, with the right renaturing conditions in place, scrambled insulin can become its native form with a 30% yield. This yield can be increased if the A and B chains are cross-linked. Secondly, through analysis of sequences of proinsulin from many species, mutations are permitted at the C chain eight times more than if it were for A and B chains.

There are various interactions that help stabilize structures of native proteins. Specifically, it is important to examine how the interactions that form protein structures are organized. In addition, there are only a small amount of possible polypeptide sequences that allow for a stable conformation. Therefore, it is evident that specific sequences are used through evolution in biological systems.

On average, about sixty percent of proteins contain a high amount of alpha helices, and beta pleated sheets. Through hydrophobic interactions, the protein is able to achieve compact nonpolar cores, but they lack the ability to specify which polypeptides to restrict in particular conformations. As seen in polypeptide segments in the coil form, the amount of hydrogen boding is not lesser than that of alpha helices and beta pleated sheets. This observation demonstrates that the different kinds of conformations of polypeptides are not limited by hydrogen bonding requirements. Ken Dill has suggested that helices and sheets occur as a result of the steric hindrance in condensed polymers. Through experimentation and simulation of conformations with simple flexible chains, it can be determined that the proportion of beta pleated sheets and alpha helices increase as the level of complication of chains is increased. Therefore, it can be concluded that helices and sheets are important in the complex structure of a protein, as they are compact in protein folding. The coupling of different forces such as hydrogen bonding, ion pairing, and van der Waals interactions further aids in the formation of alpha helices and beta sheets.

By investigating protein modification, the role of different classes of amino acid residues in protein folding can be determined. For example, in a particular study the free primary amino groups of RNase A were derivatized with poly-DL-alanine which consist of 8 residue chains. The poly-Ala chains are large in size and are water-soluble, thus allowing the RNase's 11 free amino groups to be joined without interference of the native structure of the protein or its ability to refold. As a result, it can be concluded that the protein's internal residues facilitates its native conformation because the RNase A free amino groups are localized on the exterior. Furthermore, studies have shown that mutations that occur on the surface of residues are common, and less likely to change the protein conformation compared to changes of internal residues that occur. This finding suggests that protein folding is mainly due to the hydrophobic forces.

George Rose demonstrated that protein domains consisted of subdomains, and furthermore have sub-subdomains, and etc. As a result, it is evident that large proteins have domains that are continuous, compact, and physically separable. When a polypeptide segment within a native protein is visualized as a string with many tangles, a plane can be seen when the string is cut into two segments. This process can be repeated when n/2 residues of an n-residue domain is highlighted with a blue and red color. As this process is repeated it can be seen that at all stages, the red and blue areas of the protein do not interpenetrate with one another. The following link shows an X-ray structure of HiPIP (high potential iron protein) and its first n/2 residues on the n-residue protein colored red and blue. Furthermore, the subsequent structures shown in the second and third row show this process of n/2 residue splitting reiterated as shown where the left side of the protein has its first and last halves with red and blue while the rest of the chain colored in gray. Through this example, it is clearly seen that protein structures are organized in a hierarchical way, meaning that the polypeptide chains are seen as sub-domains that are themselves compact structures and interact with adjacent structures. These interactions forms a larger well organized structure largely due to hydrogen bonding interactions and has an important role in understanding how polypeptides fold to form their native structure.

Since the side chains inside globular proteins fit together with much complementary its packing density can be almost like that of organic crystals. As a result, in order to confirm whether or not this phenomenon of high packing density was an important factor in contributing to protein structure, Eaton Lattman along with George Rose attempted to verify if there was an interaction between side chains that was preferred in a globular protein. They analyzed a total of 67 well studied structures of globular proteins, and concluded that there were no preferred interactions. This experiment demonstrated that packing is not what directs the native fold, but instead the native fold is necessary for packing of a globular protein. This notion can be further supported as members of a protein family result in the same fold despite their lack of sequence similarity and distant relationships.

In addition, structural experimental data have shown that there are a variety of ways that a protein's internal residues can become compact together in an efficient manner. In an extensive study done by Brian Matthews based on T4 lysozyme, which is produced by bacteriophage T4, it was found that changes in the residues of the T4 lysozyme only affected local shifts and did not result in any global structure change. The following link gives an X-ray view of T4 lysozyme and a brief biochemical description of the structure. Matthews took over 300 different mutants of the 164 residue T4 lysozyme, and compared them with one another. Also, it was observed that the T4 lysozyme could withstand insertions of about 4 residues while still not having any major structural changes to the overall protein structure nor enzyme activity. Furthermore, by using assay techniques it was demonstrated that only 173 of the mutants in T4 of the 2015 single residue substitutions done had significant amounts of enzymatic activity diminished. Through these experiments, it is evident that protein structures are extremely withstanding.

Levinthal's paradox is a thought experiment, also constituting a self-reference in the theory of protein folding. In 1969, Cyrus Levinthal noted that, because of the very large number of degrees of freedom in an unfolded polypeptide chain, the molecule has an astronomical number of possible conformations. An estimate of 3300 or 10143 was made in one of his papers.

The Levinthal paradox observes that if a protein were folded by sequentially sampling of all possible conformations, it would take an large amount of time to do so, even if the conformations were sampled at a rapid rate . Based upon the observation that proteins fold much faster than this, Levinthal then proposed that a random conformational search does not occur, and the protein must, therefore, fold through a series of meta-stable intermediate states.

In 1969 Cyrus Levinthal calculated that if a protein were to randomly sample every possible conformation as it folded from the unfolded state to the native state it would take an astronomical amount of time, even if the protein reached 100 billion conformations in one second. Observing that proteins fold in a relatively short amount of time, Levinthal proposed that proteins fold in a fixed and directed process. We now know that while protein folding is not a random process there does not seem to be a single fixed protein folding pathway.This observation came to be known as the Levinthal paradox. This paradox clearly reveals that proteins do not fold by trying every possible conformation. Instead, they must follow at least a partly defined folding pathway made up of intermediates between the fully denatured proteins and its native structure.

The way out of the Levinthal Paradox is to recognize cumulative selection. According to Richard Dawkins, he asked how long it would take a monkey poking randomly at a typewriter to reproduce "Methinks it is like a weasel", Hamlet's remark to Polonius. A large number of keystrokes, of the order of 1040 would be required. Yet if we suppose that each correct character was preserved, allowing the monkey to retype only the wrong ones, only a few thousand keystrokes, on average, would be needed. The crucial difference between these scenarios is that the first utilizes a completely random search whereas in the second case, partly correct intermediates are retained. This also reveals that the essence of protein folding is the tendency to retain partly correct intermediates, although the protein-folding problem is much more difficult than the one presented to Shakespeare example above.

In order to correctly understand the protein-folding problem, we must consider certain characteristics of protein. Since proteins are only marginally stable, the free-energy difference between the folded and the unfolded states of a typical 1000-residue protein is 42 kJ mol1 and thus each residue contributes on average only 0.42 kJ mol1 of energy to maintain the folded state. This amount is less than the amount of thermal energy, which is 2.5 kJ mol1 at room temperature. This meagre stabilization energy means that correct intermediates, especially those formed early in folding, can be lost. The interactions that lead to cooperative folding, nonetheless, can stabilize intermediates as structure builds up. Thus, local regions that have significant structural preference, though not necessarily stable on their own, will tend to adopt their favored structures and, as they form, can interact with one other, resulting in increased stabilization. Nucleation-condensation model refers to this conceptual framework in solving the protein-folding challenge.

Proteins folding forms energetically favorable structures stabilized by hydrophobic interactions clumping, hydrogen bonding and Van der Waals forces between amino acids. Protein folding first forms secondary structures, such as alpha helices, beta sheets, and loops. Different amino acids have different tendencies for whether they are going to form Alpha Helices, Beta sheets, or Beta Turns based upon polarity of the amino acid and rotational barriers. For example, the amino acids, valine, threonine, isoleucine, tend to destabilize the alpha helices due to steric hindrance. Thus, they prefer conformational shifts towards Beta sheets rather than alpha helices. The relative frequencies of the amino acids in secondary structures are grouped according to their preferences for alpha helices, beta sheets or turns (Table 1). Table 1: Relative frequencies of amino acid residues in secondary structuresThese structures in turn, fold to form tertiary structures, stabilized by the formation of intramolecular hydrogen bonds. Covalent bonding may also occur during the folding to a tertiary structure, through the formation of disulfide bridges or metal clusters. According to Robert Pains Mechanisms of Protein Folding, molecules also often pass through an intermediate molten globule state formed from a hydrophobic collapse (in which all hydrophobic side-chains suddenly slide inside the protein or clump together) before reaching their native confirmation. However, this means all the main chain NH and CO groups are buried in a non-polar environment, but they prefer an aqueous one, so secondary structures must fit together very well, so that the stabilization through hydrogen bonding and Van der Waals forces interactions overrides their hydrophilic tendencies. The strengths of hydrogen bonds in a protein vary depending on their position in the structure; H-bonds formed in the hydrophobic core contribute more to the stability of the native state than H-bonds exposed to the aqueous environment.

Water-soluble proteins fold into compact structures with non-polar, hydrophobic cores. The inside of protein contains non-polar residues in center (i.e. - leucine, valine, methionine and phenylalanine), while the outside contains primarily polar, charged residues (i.e. - aspatate, glutamate, lysine and arginine). This way the polar, charged molecules can interact with the surrounding water molecules while the hydrophobic molecules are protected from the aqueous surroundings. Minimizing the number of hydrophobic side chains on the outer part of the structure makes the protein structure thermodynamically more favorable because the hydrophobic molecules prefer to be clumped together, when surrounded by an aqueous environment (i.e. hydrophobic effect). Proteins that span biological membranes (i.e. - porin) have an inside out distribution, with respect to the water-soluble native structure, they have hydrophobic residue covered outer surfaces, with water filled centers lined with charged and polar amino acids.

In Folding Scene Investigation: Membrane Proteins, a paper written by Paula J Booth and Paul Curnow, the authors attempt to answer how the folding mechanisms of integral membrane proteins with helical structures work.Studying the folding of membrane proteins has always been difficult as these proteins are generally large and made of more than one subunit. The proteins posses a high degree of conformational flexibilitywhich is necessary for them to perform their function in the cell. Also, these proteins have both hydrophobic surfaces, facing the membrane, and hydrophilic surfaces, facing the aqueous regions on either side of the membrane. The proteins are move laterally and share the elastic properties of the lipid bilayer in which they are embedded. In order to study these proteins, Booth and Curnow believe that one must manipulate the lipid bilayer and combine kinetic and thermodynamic methods of investigation.

Reversible Folding and Linear Free EnergyThe free energy of protein folding is measured by reversible chemical denaturation. The reversible folding of a protein depends on this free energy. For the helix proteins that were being studied, it was proven that a reversible, two-state process is followed. bR (a helical membrane protein called bacteriorhodopsin) reversibly unfolds if SDS (a denaturant which is an anionic detergent) is added to mixed lipid, detergent micells. The two-state reaction involves a partly unfolded SDS state and a folded bR state. By comparing the logs of the unfolding and folding rate, and the SDS mole fraction, a linear plot was generated proving a linear relationship. This plot proved that bR had a very high stability outside of its membraneproving that it was unexpectedly stable. Furthermore, bR was so stable outside of the membrane that it would not unfold during a reasonable period of time without addition of denaturant.

Comparison with Water-Soluble ProteinsBooth and Curnow studied the 3 membrane proteins about which the most information is held: bR, DGK (Escherichia coli diacylglycerol kinase) and KcsA (Sterptococcus lividans potassium channel). These three membrane proteins were compared to water-soluble proteins (which fold by 2 or 3 state kinetics). The overall free energy change of unfolding in the absence of denaturant was the same for water-soluble proteins and membrane proteins of similar size. This proves that it is the balance of weak forces rather than the types of forces that stabilize the protein that determines its stability. It was proven that H-bonds in the membrane proteins were of similar strength to those of the water-soluble proteins, rather than being stronger in membrane proteins as was expected.

Mechanical Strength and Unfolding Under Applied ForceDynamic force microscopy can be used to measure the mechanical response of a particular region of a protein under applied force. The unfolding force in this case depends on the activation barrier. This unfolding has nothing to do with the thermodynamic stability of a protein. For unfolding under applied force, the membrane proteins (especially bR) seem to follow the rules of Hammond behavior. The energy difference between two consecutive states of this reaction is reduced and the states become similar in structure.

Influence of Surrounding MembraneMembrane proteins are influenced greatly by the membranes they are surrounded by. If the lipids incorporate in detergent micells-increasing the stability of the lipid structureboth the protein and its folding are stabilized. Different combinations of different lipids can result in different stabilities or folding of membrane proteins. The size of the membrane can also affect the membrane protein.Different types of lipids cause different membrane properties. A type of lipids called PE lipids have higher spontaneous curvatures than a second type of lipid called a PC lipid. By adding PE lipids to PC lipids the monolayer curvature of the bilayer increases. Increasing the curvature of the lipid bilayer increases the stability of the protein folding.

In mitochondria, the proteins that are made from the ribosomes are directly take in from the cytosol. Mitochondrial proteins are first completely synthesized in the cytosol as mitochondrial precursor proteins, then taken up into the membrane. The Mitochondrial proteins contain specific signal sequence at their N terminus. These signal sequences are often removed after entering the membrane but proteins entering membranes that has outer, inner, inter membrane have internal sequences that play a major movement in the translocation within the inner membrane.

Protein translocation plays a major role in translocating proteins across the mitochondrial membranes. Four major multi-subunit protein complexes are found in the outer and the inner membrane. TOM complexes are found in the outer membrane, and two types of TIM complexes are found integrated within the inner membrane: TIM23 and TIM22. The complexes act as receptors for the mitochondrial precursor proteins.

TOM: imports all nucleus encoded proteins. It primarily starts the transport of the signal sequence into the inter membrane space and inserts the transmembrane proteins into outer membrane space. A Beta barrel complex called the SAM complex is then in charge of properly folding the protein in the outer membrane. TIM23 found in the inner membrane moderates the insertion of soluble proteins into the matrix, and facilitates the insertion of transmembrane proteins into the inner membrane. TIM23, another inner membrane complex facilitates the insertion inner membrane proteins comprised of transporters that move ADP, ATP, and phosphate across the mitochondrial membranes. OXA, yet another inner membrane complex, helps insert inner membrane proteins that were synthesized from the mitochondria itself and the insertion of inner membrane proteins that were first transported into the matrix space.File:Translocation.jpg

The place where the protein chain begins to fold is a topic that is greatly studied. As the nascent chain goes through the exit tunnel of the ribosome and into the cellular environment, when does the chain begin to fold? The idea of cotranslational folding in the ribosomal tunnel will be discussed. The nascent chain of the protein is bound to the peptidyl transferase centre (PTC) at its C terminus and will emerge in a vectorial manner. The tunnel is very narrow and enforces a certain rigidity on the nascent chain, with the addition of each amino acid the conformational space of the protein increases. Co translational folding can be a big help in reducing the possible conformational space by helping the protein to acquire a significant level of native state while still in the ribosomal tunnel. The length of the protein can also give a good estimate of its three dimensional structure. Smaller chains tend to favor beta sheets while longer chains (like those reaching 119 out of 153 residues) tend to favor the alpha helix.

The ribosomal tunnel is more than 80 in length and its width is around 10-20 . Inside the tunnel are auxiliary molecules like the L23, L22, and L4 proteins that interact with the nascent chain help with the folding. The tunnel also has hydrophilic character and helps the nascent chain to travel through it without being hindered. Although rigid, the tunnel is not passive conduit but whether or not it has the ability to promote protein folding is unknown. A recent experiment involving cryoEM has shown that there are folding zones in the tunnel. At the exit port (some 80 from the PTC), the nascent chain has assumed a preferred low order conformation. This enforces the suggestion that the chain can have degrees of folding at certain regions. Although some low order folding can occur, the adoption of the native state occurs outside the tunnel, but not necessarily when the nascent chain has been released. The bound nascent chain (RNC) adopts partially folded structure and in a crowded cellular environment, this can cause the chain to self-associate. This self-association, however, is relieved with the staggered ribosomes lined along the exit tunnel that maximizes the distances between the RNC.

Generation of RNC for studies:

One technique of generating RNC and taking snapshots as it emerges from the tunnel is to arrest translation. A truncated DNA without a termination sequence is used. This allows for the nascent chain to remain bound until desired. To determining the residues of the chain, they can be labeled by carbon-13 or nitrogen-15 and later detected by NMR spectroscopy. Another technique is the PURE method and it contains the minimal components required for translation. This method has been used to study the interaction of the chains and auxiliary molecules like the TF chaperone. This method is coupled with quartz-crystal microbalance technique to analyze the synthesis by mass. An in vivo technique in generating RNC chain can be done by stimulating it in a high cell density. This is initially done in an unlabeled environment, the cells are then transferred to a labeled medium. The RNC is generated by SecM. The RNC is purified by affinity chromatography and detected by SDS-PAGE or immunoblotting.

By generating the RNCs, many experiments can be done to study more about the emerging nascent chain. As mentioned above, the chain emerges from the exit tunnel in a vectorial manner. This enables the chain to sample the native folding and increases the probability of folding to the native state. Along with this vectorial folding, chaperones also help in favorable folding rates and correct folding.

Protein Entering the Mammalian ER:The endoplasmic reticulum (ER) is a main checkpoint for protein maturation to ensure that only correctly folded proteins are secreted and delivered to the site of action. The protein entrance to the ER begins with recognition of a N terminus signal sequence. Specially, this sequence is detected by a signal recognition protein (SRP) causing the ribosome/nascent chain/SRP complex bind to the ER membrane. Then, the complex travels through a proteinaceous pore called Sec61 translocon which allows the polypeptide chain enter the lumen portion of the ER.

Processes in Conflict During Protein Folding:After the protein enters the ER, the proteins break up into an ensemble of folding intermediates. These intermediates take three different routes. They are either folded properly and sent to be exported out of the endoplasmic reticulum (ER) into the cytosol, aggregated or picked out for degradation. These three processes are in competition to properly secrete a protein. In order for a protein to be properly secreted, the competition between folding, aggregation and degradation must be in favor of folding, so that folding occurs faster than the other processes. This balance is termed proteostasis. The balance of proteostasis can be tipped in favor of folding by either using smaller molecules to stabilize the protein (called co-factors) or increasing the concentrations of folding factors. This ability to control proteostasis allows scientists the power to overcome some of the protein folding diseases such as cystic fibrosis.

The proteins that are folded properly are ready for anterograde transport, and secreted through the membrane of the ER into the cytosol by a cargo receptor that recognizes the properly folded protein. The proteins that are incorrectly folded are not secreted and are either targeted for degradation or aggregated. The aggregated proteins are able to re-enter the stage of protein ensembles ready to be folded so that they may try again at being folded properly.

Folding Factors in the Endoplasmic Reticulum:

Biochemical research on folding pathways has provided a comprehensive list of folding factors, or chaperones, involved with protein folding in the ER. Folding factors are categorized based on whether they catalyze certain steps or if they interact with intermediates in the folding pathway. General protein folding factors are typically separated into four different groups: heat shock proteins as chaperones or cochaperones, peptidyl prolyl cis/trans isomerases (PPIases), oxidoreductases, and glycan-binding proteins.

Many folding factors are great in that they are multi-functional. One folding factor can take care of different areas of the folding pathway. Unfortunately, this leads to redundancy due to different classes of proteins carrying out overlapping functions. This functional redundancy complicates the understanding of the specific roles of individual folding factors in aiding maturation of client proteins. Folding factors also prefer to act in concert during the maturation process, which further obscures the individual roles of each factor. Since these roles are not clear, it is difficult to confirm that even if one folding factor deals with a particular reaction in one protein, that same folding factor will carry out the same function in another.

In addition to aiding non-covalent folding and unfolding of proteins, folding factors in the ER sometimes delay interactions with the protein. This allows time for nascent proteins to fold properly and enables folded proteins to backtrack on its folding pathway, which prolongs equilibrium in a less folded state, preventing the protein from being held in a non-native state.

Folding after Endoplasmic Reticulum: Although ER provides only correctly assembled proteins to be secreted, some examples exist in which proteins change conformation in the Golgi bodies and beyond. Typically, newly folded proteins are sensitive and prone to unfolding while in the ER but resistant to unfolding after exit. In an environment without chaperones and other folding enzymes, proteins are compact and relatively resistant to change after exiting the ER. However, this doesnt necessarily mean that protein folding ends because some molecular chaperones like Hsp 70s and Hsp 90s continue to assist in protein conformation throughout the proteins existence.

A strategy for studying the folding of proteins is to unfold the protein molecules in high concentrations of a chemical denaturant like guanidinium chloride. The solution is then diluted rapidly until the denaturant concentration is lowered to a level where the native state is thermodynamically stable again. Afterwards, the structural changes of the protein folds may be observed. In theory, this sounds simple. However, such experiments are complex, since unfolded proteins have random coil states in chemical denaturants. Moreover, analyzing the structural changes taking place in a sample may is difficult, since all of the molecules may have significantly different conformations until the final stages of a reaction. As such, the analysis would have to be performed in a matter of seconds rather than days or weeks that are normally allowed to deduce the structure of a single conformation of a native protein. To avoid this problem, the disulphide bonds can be reduced after the protein is unfolded and reformed under oxidative conditions. The protein can then be identified by standard techniques such as mass spectroscopy to draw conclusions about the structure present at stages of folding where disulfide bonds are formed.

Multiple techniques are used to monitor structural changes during the refolding. For instance, in circular dichorism, UV is used from far away to provide a measurement of the appearance of the secondary structure during folding. UV at a close distance monitors the formation of the close-packed environment for aromatic residues. NMR is also a useful technique for characterizing conformations at the level of individual amino-acid residues. It can also be used to monitor how the development of structures protect amide hydrogens from solvent exchanges.

Circular Dichroism: This type of spectroscopy measures the absorption of circularly polarized light since the structures of protein such as the alpha helix and beta sheets are chiral and can absorb this sort of light. The absorption of light indicates the degree of the proteins foldedness. This technique also measures equilibrium unfolding of protein by measuring change of absorption against denaturant concentration or temperature. The denaturant melt measures the free energy of unfolding while the temperature melt measures the melting point of proteins. This technique is the most general and basic strategy for studying protein folding.

Dual Polarization Interferometry: This technique uses an evanescent wave of a laser beam confined to a waveguide to probe protein layers that have been absorbed to the surface of the waveguide. Laser light is focused on two waveguides, one that senses the beam and has an exposed surface, and one that is used to create a reference beam and to excite the polarization modes of the waveguides. The measurement of the interferogram can help calculate the protein density or fold, the size of the absorbed layer, and to infer structural information about molecular interactions at the subatomic resolution. A two-dimensional pattern is obtained in the far field when the light that has passed through the two waveguides is combined.

Mass Spectrometry: The advantages of using Mass Spectroscopy to study protein folding include the ability to detect molecules with different amounts of deuterium, which allows the heterogeneity of the protein folding reactions to be studied. It can also measure the conformation of folding intermediates bound to molecular chaperones without disrupting the complex. Mass spectrometry can also directly compare refolding properties, since mixtures of proteins can be studied without separation if the two proteins have sufficiently different molecular weights.

High Time Resolution: These are fast time-resolved techniques where a sample of unfolded protein is triggered to fold rapidly. The resulting dynamics are then studied. Ways to accomplish this include fast mixing of solutions, photochemical methods, and laser temperature jump spectroscopy.

Computational Prediction of Protein Tertiary Structure: This is a distinct form of protein structure analysis in that it involves protein folding. These programs can simulate the lengthy folding processes, provide information on statistical potential, and reproduce folding pathways.

Protein misfolding refers to the failure of a protein to achieve its tightly packed native conformation efficiently or the failure to maintain that conformation due to reduction in stability as a result of environmental change or mutation. It has been established that failure of protein folding is a general phenomenon at elevated temperatures and under other stressful circumstances. The two most common results of misfolded proteins are degradation and aggregation. When a polypeptide emerges from the cell, it may fold to the native state, degraded by proteolysis, or form aggregates with other molecules. Proteins are in constant dynamic equilibrium so even if the folding process is complete, unfolding in the cellular environment can occur. Unfolded proteins usually refold back into their native states but if control processes fail, misfolding leads to cellular malfunctioning and consequently diseases. Diseases associated with misfolding cover a wide array of pathological conditions such as cystic fibrosis where mutations in the gene encoding the results in a folding to a conformer whose secretion is prevented by quality-control mechanisms in the cell. About 50% of cancers are associated with mutations of the p53 protein that eventually lead to the loss of cell-cycle control and causing the growth of tumors. Failure of proteins to stay folded can result in aggregation, a common characteristic of a group of genetic, sporadic, and infectious conditions known as amyloidoses. Aggregation usually results in disordered species that can be degraded within the organism but it may also result in highly insoluble fibrils that accumulate in tissue. There are about twenty known diseases resulting from the formation of amyloid material including Alzheimers, Type II diabetes, and Parkinsons disease. Amyloid fibrils are ordered protein aggregates that have an extensive beta sheet structure due to intermolecular hydrogen bonds and have an overall similar appearance to the proteins they are derived from. The formation of the amyloid fibrils are the result of prolonged exposure to at least partially denatured conditions.

Alzheimer's: This neurological degeneration is caused by the accumulation of Plaques and Tangles in the nerve cells of the brain.[1] Plaques, composed of almost entirely a single protein, are aggregation of the protein beta-amyloid between the spaces of the nerve cells and Tangles are aggregation of the protein tau inside the nerve cells. Tangles are common in extensive nerve cell diseases whereas neuritic plaque is more specific to Alzheimer's. Although scientists are unsure what role Plaques and Tangles play in the formation of Alzheimer's, one theory is that these accumulated proteins impede the nerve cell's ability to communicate with each other and makes it difficult for them to survive. Studies have shown that Plaques and Tangles naturally occur as people age, but more formation is observed in people with Alzheimer's. The reasons for this increase is still unknown.

Creutzfeldt-Jakob Disease (Mad Cow Disease): This disease is caused by abnormal proteins called prions which eat away and form hole-like lesions in the brain. Prions (proteinaceous infectious virion) were discovered to be proteins with an altered conformation. Scientists hypothesize that these infectious agents could bind to other similar proteins and induce a change in their conformation as well, propagating new, infectious proteins.[2] Prions are highly resistant to heat, ultraviolet light, and radiation which makes them difficult to be eliminated. In Creutzfeldt-Jakob Disease there is an incubation period for years which is then followed by rapid progression of depression, difficulty walking, dementia and death. Currently there is no effective treatment for prion diseases and all are fatal.[3]

Parkinson's disease:A mutation in the gene which codes for alpha-synuclein is the cause of some rare cases of familial forms of Parkinson's disease. Three point mutations have been identified thus far: A53T, A30P and E46K. Also, duplication and triplication of the gene may be the cause of other lineages of Parkinson's disease.Victims of Parkinson's disease have primary symptoms that result from decreased stimulation of the motor cortex by the basal ganglia, normally caused by the insufficient formation and action of dopamine. Dopamines are produced in the dopaminergic neurons of the brain. People who suffer from this disease have brain cell loss (death of dopaminergic neurons), which may be caused by abnormal accumulation of the protein alpha-synucleinbinding to ubiquitin in the damaged cells. This makes the alpha-synuclein-ubiquitin complex unable to be directed to the proteosome. New research shows that the mistransportation of proteins between endoplasmic reticulum and the Golgi apparatus might be the cause of losing dopaminergic neurons by alpha-synuclein.

Cystic Fibrosis: Francis Collins first identified the hereditary genetic mutation in 1989. The problem occurs in the regulator cystic fibrosis transmembrane conductance regulator (CFTR), which regulates salt levels and prevents bacterial growth, when the dissociation of CFTR is disturbed as a protein regulating the chloride ion transport across the cell membrane.[4] The deleted amino acid doesn't allow bacteria in the lungs to be killed thereby causing chronic lung infections eventually leading to an early death.[5] Scientists have used nuclear magnetic resonance spectroscopy (NMR) to study Cystic Fibrosis and its effects.

Sickle Cell Anemia: Sickle-shaped red blood cells cling to walls in narrow blood vessels obstructing the flow of blood define sickle cell anemia. The shortage of red blood cells in the blood stream in addition to the lack of oxygen-carrying blood causes serious medical problems. The defect in the Hemoglobin gene is detected with the presence of two defective inherited genes. The sickle cell shape is formed as hemoglobin give up their oxygen resulting in stiff red blood cells forming rod-like structures. Some symptoms include: fatigue, shortness of breath, pain to any joint or body organ lasting for varying amounts of time, eye problems potentially leading to blindness, and yellowing of the skin and eyes which is due to the rapid breakdown of red blood cells. Luckily, sickle cell anemia can be detected by a simple blood test via hemoglobin electrophoresis. Even though there is no cure, blood transfusions, oral antibiotics, and hydroxyurea are treatments that reduce pain caused.[6]

Huntington's Disease: Also known as the trinucleotide repeat disorder, Huntington's disease results from glutamine repeats in the Huntingtin protein. Roughly 40 or more copies of C-A-G (glutamine) will result in Huntington's disease as the normal amount is between 10 and 35 copies. During the post-translational modification of mutated Huntingtin protein(mHTT), small fractions of polyglutamine expansions misfold to form inclusion bodies. Inclusion bodies are toxic for brain cell. This alteration of the Huntingtin protein does not have a definite effect except that it affects nerve cell function.[7] This incurable disease affects muscle coordination and some cognitive functions.

Cataracts: Eye lens are made up of proteins called crystallins. Crystallins have a jelly-like texture in a lens cytoplasm. The current leading cause of blindness in the world, cataracts occurs when crystallin molecules form aggregates scattering visible light causing the lens of the eye to become cloudy. UV light and oxidizing agents are thought to contribute to cataracts as they may chemically modify crystallins. In children, it has been observed that the deletion or mutation of B-crystallin facilitates cataracts formation. The likelihood of developing cataracts exponentially increases with age. Pain, Roger H. (2000). Mechanisms of Protein Folding. Oxford University Press. pp.420421. ISBN019963788. http://books.google.com/books?id=DvJygJkNCYkC&pg=PA420&lpg=PA420&dq=cataract+protein+folding&source=bl&ots=lDazpccGH2&sig=aHxuSkC1XNmcOnJYnmW4rZPuUvg&hl=en&ei=Z7rbSv3_OJG-sgOvpOGRBg&sa=X&oi=book_result&ct=result&resnum=2&ved=0CBUQ6AEwAQ#v=onepage&q=cataract%20protein%20folding&f=false. Retrieved 2009-10-18.

Protein misfolding caused by impairment in folding efficiency leads to a reduction in number of the proteins available to conduct its normal role and formation of amyloid fibrils, protein structures that aggregate, resulting in a cross- structure that can generate numerous biological functions. Protein aggregation can come from different processes occurring after translation including the increase in likelihood of degradation through the quality control system of the endoplasmic reticulum (ER), improper protein trafficking, or conversion of specific peptides and proteins from its soluble functional states into their highly organized aggregate fibrils.

Structures

X-ray Crystallography

From X-ray crystallography, three-dimensional crystals of amyloid fibril structures were formed and the structure of the peptide formation and how the molecule is packed together were examined. In one particular fragment, the crystal was found to contain parts of parallel -sheets where each peptide contributes one single -strand. The -strands are stacked and -sheets formed are parallel and side chains Asn2, Gln4 and Asn6 interact with each other in a way that water is kept out of the area in between the two -sheets with the rest of the side chains on the outside are hydrated and further away from the next -sheet.

Solid State Nuclear Magnetic Resonance (SSNMR)

Through solid-state nuclear magnetic resonance (SSNMR) and the help of other methods such as computational energy minimization, electron paramagnetic resonance and site-directed fluorescence labeling and hydrogen-deuterium exchange, mass spectrometry, limited proteolysis and proline-scanning mutagenesis the structure of an amyloid fibril was suggested to be four -sheets separated by approximately 10.

Through NMR with computational energy minimization, a 40-residue form of amyloid peptide at pH 7.4 and 24Celius was determined to contribute one pair of -strand to the core of the fibril which is connected by a protein loop. The amyloid peptides are stacked on each other in a parallel fashion.

From experiments of site-directed spin labeling coupled to electron paramagnetic resonance (SDSL-EPR), the molecule was found to be very structured in the fibrils and in parallel arrangement. SDSL-EPR along with hydrogen-deuterium exchange, mass spectrometry, limited proteolysis and proline-scanning mutagenesis suggests that the structure has high flexibility and exposure to solvent of N-terminal side, but is rigid in the other parts of the structure.

Experiments through SSNMR with fluorescence labeling and hydrogen-deuterium exchange determined that the C-terminals are involved in the core of the fibril structure with each molecule contributing four -strands with strands one and three forming one -sheet and strands two and four forming another -sheet about 10 apart.

Further experimentation approaching the atomic level with SSNMR techniques resulted in very narrow resonance lines in the spectra, showing that the molecules within fibrils hold some uniformity with peptides that display extended -strands with the fibrils.

Conclusion

The structures determined from X-ray crystallography or SSNMR were similar to previously proposed structures from cryo-electron microscopy (EM) formed from insulin. EM, which uses electron density maps, revealed untwisted -sheets in the structure. The similarities of the structures found in these experiments suggest a lot of amyloid fibrils can have similar characteristics such as the side-chain packing, aligning of -strands and separation of the -sheets.[8] Annu. Rev. Biochem. 2006.75:333-366. http://www.annualreviews.org. Retrieved 24 Oct 2011

Formation

The capability to form amyloidal protein structures that are considered to be genetic is from the findings that an increasing number of proteins show no signs of protein related diseases. It has been found that amyloidal proteins can be converted from its own protein that has a function rather than disease- related characteristics in living organisms.

In these protein mutations, different factors that affect the formation of amyloid fibril formation and different chains form amyloid fibrils at different speeds. In different polypeptide molecules, hydrophobicity, hydrophillicity, changes in charge, degree of exposure to solvent, the number of aromatic side chains, surface area, and dipole moment can affect the rate of aggregation of protein. It has been found that the concentration of protein, pH and ionic strength of the solution the protein is in as well as the amino acid sequence it is in determines the aggregation rate from the unstructured, non-homologous protein sequences.

As the hydrophobicity of the side chains increases or decreases can change the tendency for the protein to aggregate.

Charge in a protein can create aggregations through interaction of the polypeptide chain with other macromolecules around it. Also, the low tendency for -sheets to form along with the high tendency for -helixes to form contributes in facilitating amyloid formation.

It was found that the degree in which the protein sequence are exposed to solvent tend to affect the formation of amyloids. Proteins that are exposed to solvent seem to promote aggregation. Even though some other parts of the protein that had a high tendency to aggregate were not involved in the aggregation, they seem to at least be partially unexposed to the solvent but other regions that were exposed to solvent that were not involved in the aggregation had a low tendency to form amyloid fibrils.

It has even been raised that protein sequences have evolved over time to avoid forming clusters of hydrophobic residues by alternating the patterns of hydrophobic and hydrophillic regions to lower the tendency for protein aggregation to occur.[8]

The Affects of Sequence on the Formation of Amyloid Proteins

Amyloid formation arises mostly from the properties of the polypeptide chain that are similar in all peptides and proteins, but sometimes, the sequence affects the relative stabilities of the conformational states of the molecules. In that case, the polypeptide chains with different sequences form amyloid fibrils at various rates. Sequence difference affects the behavior of the protein aggression instead of affecting the stability of the protein fold. Various physicochemical factors affect the formation of amyloid structure by unfolded polypeptide chains.

Hydrophobicity of the side chains affects the aggregation of unfolded polypeptide chains. The amino acid in the regions of the aggregation site can change the ability of aggregation of a sequence when they increase or decrease the hydrophobicity at the site of the mutation or folding site. Over time, sequences have evolved to avoid creating clumps of hydrophobic residues by alternating hydrophobic areas of the protein.

Charge affects the aggregation of amyloid protein folding. A high net charge can have the possibility of impeding self association of the protein. Mutations in decreasing the positive net charge may result in the opposite effect of aggregate formation as increasing the positive net charge. It has been seen found that polypeptide chains can be run by interactions with highly charged macromolecules, displaying the importance of charge of a protein aggregation.

Secondary structures of proteins affect the amyloid aggregation as well. Studies show that a low probability to form -helix structures and a high probability to form -sheet structures are contributive factors to amyloid formation. However, it has been found that -sheet formation is not particularly favored by nature since there are little alternation of hydrophilic and hydrophobic residue sequence patterns to be found.

The characteristics of the amino acid sequences affect the amyloid fibril structure and rate of aggregation. Different mutations, including changes in the number of aromatic side chains, the amount of exposed surface area and dipole moment, have been said to change the aggregation rates of lots of polypeptide chains.

Unfolded regions play vital roles in promoting the aggregation of partially folded proteins. Some regions that were found to be flexible or exposed to solvent were fond of aggregation. Other regions that are not involved in the aggregation were found to not be exposed, but rather half buried even though they have high possibility of aggregating while the exposed regions of the structure that are not involved in the aggregation have a low probability of aggregating amyloid fibrils. The fibrils tend to come together by association of unfolded polypeptide segments rather than by docking the structural elements.

Overall, it has been found that unfolded proteins have lower less hydrophobicity and higher net charge than that of a folded protein. Residues that tend not to form the secondary structure of -sheet structured proteins seem to inhibit the occurrence of amyloid aggregation. Concentration of protein, pH and ionic strength were found to be associated with the amino acid sequence, which affects the rate of aggregation.

[8]

It is understood that the primary structure (the amino acid sequence) of a protein predisposes the protein for a specific three dimensional structure and how it will fold from the unfolded form to the native state. The concentration of salts, the temperature, the nature of the primary solvent, macromolecular crowding, and the presence of chaperones are all factors that affect the mechanism of folding and the ratio of unfolded proteins to those in the native state. More than anything, these environmental factors affect the likelihood of any single protein reaching the correct final structure.

Isolated proteins placed in proper environments (specific solvent, solute concentrations, pH, temperature, etc.) tend to self-fold into the correct native conformation. Altering any of these environmental characteristics can disrupt the structure and/or interfere with the folding mechanism. A pH outside the normal range of a given protein can ionize specific amino acids or interfere with both polar and dipole-dipole intramolecular forces that would otherwise stabilize the structure. Excess heat (cooking) proteins can break hydrogen bonds essential to the secondary structure of proteins.

Extreme environments or the presence of chemical denaturants (such as reducing agents that can break disulfide bonds) can cause proteins to denature and lose its secondary and tertiary structure, forming into a random coil. Under certain conditions fully denatured proteins can return to their native state. Intentional denaturing is used in various methods to analyze biomolecules.

The complex environments within cells often necessitate chaperones and other biomolecules for proteins to properly form the native state.

Protein is an essential part of living thing. The development of human body is needed to be parallel with the development of protein. But protein contains so many mysteries that we did not discovery yet. For example, that is protein folding. Folding is a necessary activity of proteins. They need to fold to continue their biological activity. Folding is also a process that very protein goes through to have a stable conformation. But sometimes this process is happened incorrectly, and the scientist call this problem is protein misfolding. The results of protein folding incorrectly are so many bad diseases happening for human, animals and living things such as Alzheimers disease and Mad Cow disease. Because of this reason, the researches about protein folding and misfolding become very important. During the process of discovering about protein, folding, misfolding and its affects, the scientists have been collecting many successes; the mystery about protein is unraveled gradually. As a scientist, W. A. (Bill) Thomasson records many importance things about protein in the article Unraveling the Mystery of Protein Folding; in this article, he make the points about Alzheimers disease and Mad Cow disease and some affects of protein misfolding beside the successes of science about them.Dr Thomasson begins his article by introduce generally about protein folding and misfolding. First of all, proteins consists the sequences of amino acid. The scientists have discovered 20 amino acids appearing in proteins. The protein structure is known with 2 basic shapes which are _helix and _sheet. Most of proteins probably go through several intermediate states on their way to a stable conformation (Campbell and Reece, 79). Proteins need to fold to continue its activity. The scientists have listed 3 type of protein folding; the protein can be folded, partial folded or misfolded. In the process of folding, the proteins called chaperones are associated with the target protein; however once folding is complete (or even before) the chaperone will leave its current protein molecule and go on to support the folding of another (Thomasson). The author of the article records the very important conclusion of Anfinsen about protein misfolding. In his point of view, the misfolding is occurred in the process of folding when the folding goes wrong. The research of protein misfolding is focus on the temperature sensitive mutation; the scientists observe the bacteriophage P22 with the changing of temperature to cause the mutation. And they conclude that the mutant proteins are less stable than the normal. It means, they give a conclusion is that in the tailspike of bacteriophage the misfolded proteins is less stable than the correctly folded proteins and they are difficult to reach the properly folded state. When the protein misfolding occurs, it results many bad disease. The aggregation can appear along with the appearance of misfolding and it is at the brain to cause Alzheimers disease and Mad Cow disease as many scientists consider. One affect of protein misfolding on human life that is Alzheimers disease. This is a disease of the elderly. According to the research of scientist, this disease is occurred when the amyloid precursor protein is misfolding. This protein is processed into a soluble peptide A. The scientists have not known exactly the reason of this disease yet. But the main reason causing the misfolding is the protein apolipoprotein E (apoE) inside our blood stream. The protein apoE has three forms such as apoE2, apoE3 and apoE4. The affects of each form of apoE on the A is not discovered yet but the scientists consider that the apoE can bind to the A. In the process of misfolding, the -amyloid is formed to make neuritic plaque in the Alzheimers patient. This disease is just happened with the older people because in the amyloid process, a nucleus is formed very slowly. The mutation of this protein is not stable and causes the disease. The studying about apoE is still a secret because some scientists show that one form of this protein is developing the disease but another form is decreasing the development of the disease. Finally, the research about Alzheimers disease is continued in order to affirm the results of protein apoE on A and to find the treatment for this disease successfully. Another affect from the protein misfolding is the Mad Cow disease. This is a very dangerous disease because it can be transmitted from animals to human. This disease causes by the misfolding of prions. The process of misfolding is the self-replicating of the prions. Prions are protein particles containing DNA and RNA. The mutation appear in the process of folding, the prions self-replicate and cause the misfolding of the proteins. They contain DNA and RNA. This is a special situation of the protein; it can be served as its chaperons. Because of the replicating, the prion was multiplied very quickly along with the increasing of normal proteins. This disease shows that the protein folding can be occurred without the genetics such as the experiment on the sheep. Dr. Thomasson continues his article by some more information about the misfolding and the way of the scientist to prove the mystery. He gives the information about the protein p53 and its mutation. It can cause the cancer, it also one type of protein misfolding. The point Dr. Thomasson wants to make that is his idea about the drug that can make the protein misfolding becoming more stable and minimize the misfolding of protein. This idea seems very good but its results are like a mystery as the mystery of protein folding. The research about the protein folding is very important to our lives. The misfolding is one of the main reasons causing so many dangerous disease but we did not have a successful treatment yet. The study of protein folding is more and more successful to help the human to be able to destroy the disease causing by misfolding. The disease caused by protein misfolding has become one problem of human that need to be solved.

Molecular Chaperones are known mainly for assisting the folding of proteins. Chaperones are not just involved in the initial stages of a proteins life. Molecular Chaperones are involved in producing, maintaining, and recycling the structure and units of protein chaperones. Chaperones are present in the cytosol but are also present in cellular compartment such as the membrane bounded mitochondria and endoplasmic reticulum. The role or necessity of chaperones to the proper folding of proteins varies. Many prokaryotes have few chaperones and less redundancy in the types of chaperones and whereas eukaryotes have large families of chaperones containing some redundancy. It is hypothesized that some chaperones are essential to proper protein folding such as the example of the prokaryote which has less variations of a chaperone family available. Other chaperones play less of an essential role such as in eukaryotes where more variations within a family of chaperones exist and gradients of efficiency or affinity are produced. This redundancy or existence of less efficient chaperones may exist in one state but the effectiveness of chaperones is also a function of their environment. The pH, space, temperature, protein aggregation and other external factors may render a chaperone that was once ineffective into a more essential chaperone. These environmental factors show why it is important to simulate cellular in vivo conditions, or native states, in order to grasp the conditions that require use of chaperones. This briefly summarizes the difficulties in analyzing and comparing chaperone function in vivo vs. in vitro.Simulating in vivo, or the environment within the cell, is important not just because of physical factors such as pH or temperature but also because the time in which the chaperone begins to conform the polypeptide. Some chaperones are nearby the ribosome and attach immediately to the polypeptide to prevent misconformation. Other chaperones allow the polypeptide to begin folding by itself and attach later on. Thus the role of each chaperone becomes specific to its vicinity to the polypeptide and time and place in which it assists folding. Recent research has implicated that chaperones within the nucleolus not only catalyze protein folding but also catalyze other functions important to maintain a healthy cell. These nucleolar chaperones are called Nucleolar Multitasking Proteins (NoMP's). Heat shock proteins, for example, not only help other proteins fold but also act during moments of stress to regulate protein homeostatis. Furthermore, there is evidence that chaperones work together in networks to oversee certain functions like dealing with toxins, starvation or infection.

The nucleolar chaperone network is divided into different branches that have specific functions. The network is dynamic and can vary in concentration or location of the network components depending on changes in the physiology and environment of the cell. Heat shock proteins (HSPs), which are classified based on their molecular weights, are integral components of the chaperone network. HSP 70s and 90s maintain proteostasis by ensuring that proteins are properly folded and preventing proteotoxicity, which is the damage of a cell function due to a misfolded protein. HSP70s help to fold recently synthesized proteins, while HSP90s help later in the folding process. The nucleolar network also contains chaperones that are part of ribosome biogenesis, or the synthesis of ribosomes in the cells. Proteins in the HSP70 and DNAJ families, which help to process pre-rRNA, are regularly found in protein complexes that process pre-rRNA in Saccharomyces cerevisiae (a species of yeast). Other HSPs are important in ribosome biogenesis as well, including HSP90 which works together with TAH1 and PIH1 to create small nucleolar ribonucleoproteins. The nucleolar chaperone network provide the organization and assistance needed to complete the biological taks necessary for cell survival, and if it does not function properly there can be many problems. For instance, when cancer cells have increased levels of rRNA synthesis, ribosome biogenesis is increased. Scientists are researching the compound CX-3543, which can stop nucleolin from binding with rDNA and impede RNA synthesis, leading to cell death. It is possible to potentially use drugs designed to target specific branches of the nucleolar chaperone network in malfunctioning cells. Other networks of chaperones include networks that specifically participate in de novo protein folding, meaning they help to fold newly made proteins, and the refolding of proteins that have been damaged. One chaperone network that exists in tumor cell mitochondria contains HSP90 and TRAP1, which protect the mitochondria and prevent cell death, allowing the cancer cells to continue to spread uncontrollably.[9]

HSP 70 is a protein in the Heat Shock Protein family along with HSP 90. It works together with HSP 90 to support protein homeostasis. It binds to newly synthesized proteins early in the folding process. It has three major domains, the N-terminal ATPase domain, the Substrate binding domain, and C-terminal domain. The N-terminal ATPase binds and hydrolyzes ATP, the substrate binding domain hold an affinity for neutral, hydrophobic amino acid residues up to seven residues in length while the c-terminal domain acts as a sort of lid for the substrate binding domain. This lid is open when HSP 70 is ATP bound and closes when hsp 70 is ADP bound. HSP70, or DnaK, are bacterial chaperones and can help in folding by clamping down on a peptide.[10]

GroEL and GroES, or 60kDa and 10kDa, are both bacterial chaperones. Both GroEL and GroES are structured so that they are a stacked ring with an empty center. The protein fits in this hollow center. Conformational changes within the chamber can then change the shape and folding of the protein.[10]

HSP 90 is a protein in the Heat Shock Protein family. This particular protein, however, is different from other chaperones in that HSP90 is limited in the folding aspect of molecular chaperones. Instead, Hsp 90 is vital to study and understand because many cancer cells have been able to take over and utilize the Hsp 90 in order to survive in many virulent surroundings. Therefore, if one were to structurally study and somehow target Hsp90 inhibitors, then there could be a way to stop cancer cells from spreading. Furthermore, many studies have been performed in order to test whether or not the Hsp 90 chaperone cycle is driven by ATP binding and hydrolysis or some other factor. But after much research by Southworth and Agard, there was enough evidence to state that HSP90 protein could conformationally change without nucleotide binding but rather the stabilization of an equilibrium is the factor that will change the Hsp90 to a closed or compact or open state. The three conformations of the Hsp90 were found through x-ray crystallography and also through single electron particle microscopy and by studying the three-state conformational changes in yeast Hsp90, human Hsp90 and bacteria Hsp 90 (HtpG) it was clear that there are distinct conformational changes for specific species. Overall, Hsp90 is a chaperone that is more involved with maintaining homeostasis within a cell rather than the involvement of protein folding. Hsp90 has rising potential in the area of drug development in the future since it plays such an essential role in aiding the survival for cancer cells.

This is the first chaperone to interact with the nascent chain as it exits the ribosome tunnel. Without the nascent chain, the TF cycles on and off but once the nascent chain is present, it binds onto the chain, forming a protecting cavity around. In order to do its function, TF scans for any exposed hydrophobic segment of the nascent chain and it can also re-associate with the chain. Folding is found to be more efficient in the presence of the TF, however, this is done at the expense of speed, it can stay with the chain for more than 30 seconds. The release of the chain is triggered when the hydrophobic portions is buried as the folding progresses toward the native state.

YidC, Alb3, and Oxa1 are proteins that facilitate the insertion of proteins in the plasma membrane. YidC is a protein that has only two polypeptide chains. The formation of its structure has been supported by particular phospholipids. YidC proteins can be found in Gram-negative and Gram-positive bacteria. Oxa1 can be found in the inner membrane of the mitochondria. Alb3 locates in the membrane of the thylakoid inside the chloroplast. Experiments showed that YidC protein actively contributes to the insertion of Pf3 coat protein. In addition, YidC also has direct contact with the hydrophobic segment of Pf3 coat protein. Although Oxa1 can only be found in the mitochondria it can also facilitate the insertion of membrane proteins in the nucleus. The role of YidC and Alb3 seems to be interchangeable because Alb3 can replace YidC in E. coli. Moreover, YidC, Oxa1, and Alb3 all support the insertion of Sec-independent proteins. Oxa1 only supports the insertion of Sec-independent proteins because the mitochondria in yeast cell do not have Sec proteins.

Nucleotide-binding domains that are leucine- rich (NLR) provide a pathogen-sensing mechanism that is present in both plants and animals. They could either be triggered directly or indirectly by a derivation of pathogen molecules via elusive mechanisms. Researches show that molecular chaperones like HSP90, SGT1, and RAR1 are main stabilizing components for NLR proteins. HSP90 can monitor the function of its corresponding clients that apply to NLR proteins in three practical ways: promotion of steady-state of functional threshold, activating stimulus-dependent activity, and raising the capacity to evolve.

Plants contain many NLR genes that considered being polymorphic in the LRR domain in order to be familiar with the highly diversified pathogen effectors. The NLR sensor stability will be the mechanism that will determine the pathogen recognition. The HSP90 system is advantageous for plants because it will couple metastable NLR proteins and stabilize them in a signaling competent condition. This will allow for the masking of mutations that would be detrimental.

It is known that chaperones work together to aid in the folding of protein in order to prevent misfolding. However, the mechanism of how chaperones help in protein folding was not fully understood. Recent studies on Hsp40 and Hsp70 have provided more insights into the mechanism of chaperones and their substrate. The Hsp40 family consists of many Hsp40 with different J-domain. Different J-domain will carry out different Hsp70 ATPase activities when Hsp40 binds to Hsp70. In protein folding, an unfolded polypeptide binds to a Hsp40 co-chaparone. From there, the J-domain of Hsp40 binds to the nucleotide-binding domain (NBD) of Hsp70. A conformation change in the Hsp70 substrate-binding domain occurs when the hydrolysis of ATP to ADP takes place on the HSP70 NBD. This causes Hsp70 to have a higher affinity for the polypeptide substrate and unbind the substrate from Hsp40. When ADP is exchange for ATP, the polypeptide substrate is released from Hsp40. Studies have shown that nucleotide exchange factors make changes to the lobe on the Hsp70 ATPASE domain in way that decreases Hsp70s affinity for ADP. Once the polypeptide is released from Hsp70, it can fold to its native state or it can be refolded by the chaperones if there is a misfolding. If a polypeptide that is bounded to Hsp70 is recognized by E3 ubiquitin ligase CHIP, it will be degraded.[11]

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Structural Biochemistry/Proteins/Protein Folding ...

After a polypeptide is produced in protein synthesis, it's not necessarily a functional protein yet! Explore protein folding that occurs within levels of protein structure with the Amoeba Sisters! Primary, secondary, tertiary, and quaternary protein structure levels are briefly discussed. Video also mentions chaperonins (chaperone proteins) and how proteins can be denatured.

Table of Contents:0:41 Reminder of Protein Roles1:06 Modifications of Proteins1:25 Importance of Shape for Proteins1:56 Levels of Protein Structure2:06 Primary Structure3:10 Secondary Structure3:45 Tertiary Structure4:58 Quaternary Structure [not in all proteins]6:01 Proteins often have help in folding [introduces chaperonins]6:40 Denaturing Proteins

*Further Reading Suggestions*

Related to Protein Misfoldings:

https://www.nature.com/scitable/topic...https://www.scientificamerican.com/ar...

Learn About "The Protein Folding Problem":https://www.ncbi.nlm.nih.gov/pmc/arti...

Factual References:

OpenStax, Biology. OpenStax CNX. Jun 1, 2018 http://cnx.org/contents/185cbf87-c72e....

Reece, J. B., & Campbell, N. A. (2011). Campbell biology. Boston: Benjamin Cummings / Pearson.

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Protein Structure and Folding

Introduction and Protein Structure

Proteins have several layers of structure each of which is important in the process of protein folding. The first most basic level of this structure is the sequence of amino acids themselves.1 The sequencing is important because it will determine the types of interactions seen in the protein as it is folding. A novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core.2 This study shows that not only is the amino acids that are in a protein important but also the order in which they are sequenced. The interactions of the amino acids will determine what the secondary and tertiary structure of the protein will be.

The next layer in protein structure is the secondary structure. The secondary structure includes architectural structures that extend in one dimension.1 Secondary structure includes -Helixes (Figure 1) and -sheets (Figure 2). The -helices, the most common secondary structure in proteins, the peptide CONHgroups in the backbone form chains held together by NH OC hydrogen bonds.3 The -helices form the backbone of proteins and help to aid in the folding process. The -sheets form in two distinct ways. They are able to form in both parallel -pleated sheets and anti parallel -pleated sheets.1 When the -helix or -sheet is formed, the excluded volumes generated by the backbone and side chains overlap, leading to an increase in the total volume available to the translational displacement of water molecules.4 This is important because it leads to a more thermodynamically stable conformation and leads to less strain on the protein as a whole and thus are aided by the conformation.

Figure 1: (left) typical example to an -helix, from Wikimedia CommonsFigure 2: (right) typical example of an -sheet, from Wikimedia Commons

The tertiary structure is the next layer in protein structure. This takes the -Helixes and -sheets and allows them to fold into a three dimensional structure.1 Most proteins take on a globular structure once folded. The description of globular protein structures as an ensemble of contiguous closed loops or tightened end fragments reveals fold elements crucial for the formation of stable structures and for navigating the very process of protein folding.5 The globular proteins generally have a hydrophobic core surrounded by a hydrophilic outer layer. These interactions are important because they lead to the global structure and help create channels and binding sites for enzymes.

The last layer of protein structure is the quaternary structure. The folding transition and the functional transitions between useful states are encoded in the linear sequence of amino acids, and a long- term goal of structural biology is to be able to predict both the structure and function of molecules from the information in the sequence.6 The Subunit organization is the last level of structure in protein molecules.1 The organization of the subunits is important because that determines the types of interactions that can form and dictates its use in the body.

Proteins are folded and held together by several forms of molecular interactions. The molecular interactions include the thermodynamic stability of the complex, the hydrophobic interactions and the disulfide bonds formed in the proteins. The figure below (figure 3) is an example of protein folding.

Figure 3: Protein Folding, from Wikimedia Commons

The biggest factor in a proteins ability to fold is the thermodynamics of the structure. The interaction scheme includes the short-range propensity to form extended conformations, residue-dependent long-range contact potentials, and orientation-dependent hydrogen bonds.7 The thermodynamics are a main stabilizing force within a protein because if it is not in the lowest energy conformation it will continue to move and adjust until it finds its most stable state. The use of energy diagrams and maps are key in finding out when the protein is in the most stable form possible.

The next type of interaction in protein folding is the hydrophobic interactions within the protein. The framework model and the hydrophobic collapse model represent two canonical descriptions of the protein folding process. The first places primary reliance on the short-range interactions of secondary structure and the second assigns greater importance to the long-range interactions of tertiary structure.6 These hydrophobic interactions have an impact not just on the primary structure but then lead to changes seen in the secondary and tertiary structure as well. Globular proteins acquire distinct compact native con- formations in water as a result of the hydrophobic effect.7 When a protein has been folded in the correct way it usually exists with the hydrophobic core as a result of being hydrated by waters in the system around it which is important because it creates a charged core to the protein and can lead to the creation of channels within the protein. The hydrophobic interactions are found to affect time correlation functions in the vicinity of the native state even though they have no impact on same time characteristics of the structure fluctuations around the native state.7 The hydrophobic interactions are shown to have an impact on the protein even after it has found the most stable conformation in how the proteins can interact with each other as well as folding themselves.

Another type of interaction seen when the protein is folding is the disulfide linkages that form in the protein. (See figure 4) The disulfide bond, a sulfur- sulfur chemical bond that results from an oxidative process that links nonadjacent (in most cases) cysteines of a protein.9 These are a major way that proteins get into their folded form. The types of disulfide bonds are cysteine-cysteine linkage is a stable part of their final folded structure and those in which pairs of cysteines alternate between the reduced and oxidized states.9 The more common is the linkages that cause the protein to fold together and link back on itself compared to the cysteines that are changing oxidation states because the bonds between cysteines once created are fairly stable.

Figure 4: Disulfide Bonds, shown in the picture in yellow, from Wikimedia Commons

Proteins can miss function for several reasons. When a protein is miss folded it can lead to denaturation of the protein. Denaturation is the loss of protein structure and function.1 The miss folding does not always lead to complete lack of function but only partial loss of functionality. The miss functioning of proteins can sometimes lead to diseases in the human body.

Alzheimer's Disease (AD) is a neurological degenerative disease that affects around 5 million Americans, including nearly half of those who are age 85 or older.10 The predominant risk factors of AD are age, family history, and heredity. Alzheimers disease typically results in memory loss, confusion of time and place, misplacing places, and changes in mood and behavior.11 AD results in dense plaques in the brain that are comprised of fibrillar -amyloid proteins with a well-orders -sheet secondary structure.12 These plaques visually look like voids in the brain matter (see figure 5) and are directly connected to the deterioration of thought processes. It has been determined that AD is a protein misfolding disease, where the misfolded protein is directly related to the formation of these plaques in the brain.13

Figure 5: Comparison of healthy brain (left) with brian with Alzheimer's (right)From Wikimedia Commons

It is yet to be fully understood what exactly causes this protein misfolding to begin, but several theories point to oxidative stress in the brain to be the initiating factor. This oxidation results in damage to the phospholipids in the brain, which has been found to result in a faster accumulation of amyloid -proteins.14

Figure 6: Beta-Amyloid Plaque Formation, from Wikimedia Commons

Cystic Fibrosis (CF) is a chronic disease that affects 30,000 Americans. The typical affects of CF is a production of thick, sticky mucus that clogs the lungs and leads to life-threatening lung infection, and obstructs the pancreas preventing proper food processing.15 CF is caused by protein misfolding. This misfolding then results in some change in the protein known as cystic fibrosis transmembrane conductance regulator (CFTR), which can result in this potentially fatal disease.16 In approximately 70% of CF cases, a deletion of phenylalanine at position 508 in the CFTR is deleted. This deletion of Phe508 seems to be directly connected to the formation of CF.17 The protein misfolding that results in CF occurs prior to birth, but it is not entirely clear as to why.

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Protein Folding - Chemistry LibreTexts

We often think of proteins as nutrients in the food we eat or the main component of muscles, but proteins are also microscopic molecules inside of cells that perform diverse and vital jobs. With the Human Genome Project complete, scientists are turning their attention to the human proteome, the catalog of all human proteins. This work has shown that the world of proteins is a fascinating one, full of molecules with such intricate shapes and precise functions that they seem almost fanciful.

A proteins function depends on its shape, and when protein formation goes awry, the resulting misshapen proteins cause problems that range from bad, when proteins neglect their important work, to ugly, when they form a sticky, clumpy mess inside of cells. Current research suggests that the world of proteins is far from pristine. Protein formation is an error-prone process, and mistakes along the way have been linked to a number of human diseases.

There are 20,000 to over 100,000 unique types of proteins within a typical human cell. Why so many? Proteins are the workhorses of the cell. Each expertly performs a specific task. Some are structural, lending stiffness and rigidity to muscle cells or long thin neurons, for example. Others bind to specific molecules and shuttle them to new locations, and still others catalyze reactions that allow cells to divide and grow. This wealth of diversity and specificity in function is made possible by a seemingly simple property of proteins: they fold.

A protein starts off in the cell as a long chain of, on average, 300 building blocks called amino acids. There are 22 different types of amino acids, and their ordering determines how the protein chain will fold upon itself. When folding, two types of structures usually form first. Some regions of the protein chain coil up into slinky-like formations called alpha helices, while other regions fold into zigzag patterns called beta sheets, which resemble the folds of a paper fan. These two structures can interact to form more complex structures. For example, in one protein structure, several beta sheets wrap around themselves to form a hollow tube with a few alpha helices jutting out from one end. The tube is short and squat such that the overall structure resembles snakes (alpha helices) emerging from a can (beta sheet tube). A few other protein structures with descriptive names include the beta barrel, the beta propeller, the alpha/beta horseshoe, and the jelly-roll fold.

These complex structures allow proteins to perform their diverse jobs in the cell. The snakes in a can protein, when embedded in a cell membrane, creates a tunnel that allows traffic into and out of cells. Other proteins form shapes with pockets called active sites that are perfectly shaped to bind to a particular molecule, like a lock and key. By folding into distinct shapes, proteins can perform very different roles despite being composed of the same basic building blocks. To draw an analogy, all vehicles are made from steel, but a racecars sleek shape wins races, while a bus, dump truck, crane, or zamboni are each shaped to perform their own unique tasks.

Folding allows a protein to adopt a functional shape, but it is a complex process that sometimes fails. Protein folding can go wrong for three major reasons:

1: A person might possess a mutation that changes an amino acid in the protein chain, making it difficult for a particular protein to find its preferred fold or native state. This is the case for inherited mutations, for example, those leading to cystic fibrosis or sickle cell anemia. These mutations are located in the DNA sequence or gene that encodes one particular protein. Therefore, these types of inherited mutations affect only that particular protein and its related function.

2: On the other hand, protein folding failure can be viewed as an ongoing and more general process that affects many proteins. When proteins are created, the machine that reads the directions from DNA to create the long chains of amino acids can make mistakes. Scientists estimate that this machine, the ribosome, makes mistakes in as many as 1 in every 7 proteins! These mistakes can make the resulting proteins less likely to fold properly.

3: Even if an amino acid chain has no mutations or mistakes, it may still not reach its preferred folded shape simply because proteins do not fold correctly 100% of the time. Protein folding becomes even more difficult if the conditions in the cell, like acidity and temperature, change from those to which the organism is accustomed.

A failure in protein folding causes several known diseases, and scientists hypothesize that many more diseases may be related to folding problems. There are two completely different problems that occur in cells when their proteins do not fold properly.

One type of problem, called loss of function, results when not enough of a particular protein folds properly, causing a shortage of specialized workers needed to do a specific job. For example, imagine that a properly folded protein is perfectly shaped to bind a toxin and break it into less toxic byproducts. Without enough of the properly folded protein available, the toxin will build up to damaging levels. As another example, a protein may be responsible for metabolizing sugar so that the cell can use it for energy. The cell will grow slowly due to lack of energy if not enough of the protein is present in its functional state. The reason the cell gets sick, in these cases, is due to a lack of one specific, properly folded, functional protein. Cystic fibrosis, Tay-Sachs disease, Marfan syndrome, and some forms of cancer are examples of diseases that result when one type of protein is not able to perform its job. Who knew that one type of protein among tens of thousands could be so important?

Proteins that fold improperly may also impact the health of the cell regardless of the function of the protein. When proteins fail to fold into their functional state, the resulting misfolded proteins can be contorted into shapes that are unfavorable to the crowded cellular environment. Most proteins possess sticky, water-hating amino acids that they bury deep inside their core. Misfolded proteins wear these inner parts on the outside, like a chocolate-covered candy that has been crushed to reveal a gooey caramel center. These misfolded proteins often stick together forming clumps called aggregates. Scientists hypothesize that the accumulation of misfolded proteins plays a role in several neurological diseases, including Alzheimers, Parkinsons, Huntingtons, and Lou Gehrigs (ALS) disease, but scientists are still working to discover exactly how these misfolded, sticky molecules inflict their damage on cells.

One misfolded protein stands out among the rest to deserve special attention. The prion protein in Creutzfeldt-Jakob disease, also known as mad cow disease, is an example of a misfolded protein gone rogue. This protein is not only irreversibly misfolded, but it converts other functional proteins into its twisted state.

Recent research shows that protein misfolding happens frequently inside of cells. Fortunately, cells are accustomed to coping with this problem and have several systems in place to refold or destroy aberrant protein formations.

Chaperones are one such system. Appropriately named, they accompany proteins through the folding process, improving a proteins chances of folding properly and even allowing some misfolded proteins the opportunity to refold. Interestingly, chaperones are proteins themselves! There are many different types of chaperones. Some cater specifically to helping one type of protein fold, while others act more generally. Some chaperones are shaped like large hollow chambers and provide proteins with a safe space, isolated from other molecules, in which to fold. Production of several chaperones is boosted when a cell encounters high temperatures or other conditions making protein folding more difficult, thus earning these chaperones the alias, heat shock proteins.

Another line of cell defense against misfolded proteins is called the proteasome. If misfolded proteins linger in the cell, they will be targeted for destruction by this machine, which chews up proteins and spits them out as small fragments of amino acids. The proteasome is like a recycling center, allowing the cell to reuse amino acids to make more proteins. The proteasome itself is not one protein but many acting together. Proteins frequently interact to form larger structures with important cellular functions. For example, the tail of a human sperm is a structure composed of many types of proteins that work together to form a complex rotary engine that propels the sperm forward.

Why is it that some misfolded proteins are able to evade systems like chaperones and the proteasome? How can sticky misfolded proteins cause the neurodegenerative diseases listed above? Do some proteins misfold more often than others? These questions are at the forefront of current research seeking to understand basic protein biology and the diseases that result when protein folding goes awry.

The wide world of proteins, with its great assortment of shapes, bestows cells with capabilities that allow for life to exist and allow for its diversity (e.g., the differences between eye, skin, lung or heart cells, and the differences between species). Perhaps for this reason, the word protein is from the Greek word protas, meaning of primary importance.

Contributed by Kerry Geiler, a 4th year Ph.D student in the Harvard Department of Organismic and Evolutionary Biology

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