Category Archives: Protein Folding

For many of us, we are not quite getting the sleep we need. Whether that is because of obligations at work, school, or with your family, the end result is often the same: waking up not feeling quite rested.

As a graduate student, I know this too well. I find the poem below by Edna St. Vincent Millay to represent the life I am currently living.

My candle burns at both ends; It will not last the night;But ah, my foes, and oh, my friends It gives a lovely light!

First Fig by Edna St. Vincent Millay

However, despite the seemingly attractiveness of a lovely light, the reality is that one cannot continue indefinitely or in a healthy manner without quality sleep. In fact, short sleep duration over a continual basis may lead one to a higher risk of developing cardiovascular disease, diabetes, depression, and dementia among other chronic conditions.

So how exactly can we maximize on this daily event to lead healthier lives? While the number of hours will vary depending on your age, health experts recommend that adults get at least 7 hours of sleep every night. But is 7 hours with your eyes closed quality sleep? In a given sleep cycle, your brain will move through different stages of electrical activity that lasts on average 90 minutes. Supplemental and more important than the number of hours you sleep is the quality of that sleep and the time spent in restorative stages.

Stage 1 (N1) is that initial stage of drifting off, and will only last a short period of time. The subsequent Stage 2 (N2) is what follows if undisturbed, and allows the body to slow down and relax. This stage can last between 10 minutes to a half hour, and most of your sleep time is spent here. Arguably the most import sleep stage, Stage 3 (N3), comes next and is know as deep sleep. During this stage, your body is able to not only recover from the day but boost your immune system and clear toxins from your brain! Finally, the last stage of the sleep cycle is the rapid eye movement (REM) stage, which has almost wake-like levels of brain activity. In REM sleep, which typically gets longer as the night goes on, we boost our cognitive functions like memory and creative thinking.

The Sleep Foundation provides an extensive list of ways to produce a healthy nights sleep, which include the creation of a sleep-inducing bedroom and being mindful of pro-sleep habits when we are awake during the day. I encourage you to take a look at them, and consider how you might be able to incorporate some/all of these items to improve your own sleep.

For me, reading a scientific manuscript before bed (and sometimes during the day) generally has a sleep-inducing effect. If you too can relate, or simply would like to learn about the awesome work being published in ASBMB journals related to sleep, take a look at the articles below!

Effects of sleep restriction on post-dinner metabolism: Researchers from Penn State and Harvard examined the effects of only 5 hours of sleep per night on after dinner metabolism following a high fat meal. Fifteen healthy men were evaluated for post-prandial lipemia, glycemia, and enteric hormonal and inflammatory responses following lunches and dinners with high calories from fat. The team concluded that sleep restriction impaired post-meal blood lipid levels and decreased satiety. If you need more information to feel satisfied, read on their findings here.

Bridging DAT sleep gap: Researchers from Vienna, Austria attempted to explore the molecular mechanism underlying juvenile dystonia and parkinsonism with regard to mutations in the human dopamine transporter (hDAT) by examining 13 mutants of DAT that are known to cause such disease and their ability to be pharmacologically rescued. The Drosophila models used have evolutionary conservation in respect to dopaminergic neurotransmission, and DAT deficiency results in reduced sleep for Drosophila. Their results identified protein folding deficits from specific DAT mutations that can be rescued with a chaperone compound to potentially prevent disease manifestation in affected children. Take a look for yourself here.

Regulation of molecular clockmakers: A team of researchers from Turkey used computational and biochemical techniques to screen and evaluate small molecules that modulate the regulatory proteins involved in circadian rhythm to improve management of sleep disorders. In many of these sleep disorders and other chronic medical conditions, a decline in the amplitude of the circadian rhythm is observed. The team found a molecule, CDK8, stabilized part of a transcriptional complex that allows for enhanced amplitude of the circadian rhythm. They believe this compound can serve as a tool for a variety of therapeutic areas. Read more about their work here.

Read the rest here:
Rest and relaxation - ASBMB Today

Did you miss a session at the Data Summit? Watch On-Demand Here.

This article is contributed by Rick Hao, lead deep tech partner at pan-European VCSpeedinvest.

With an annual growth rateof 44%, the market for AI and machine learning is drawing continued interest from business leaders across every industry. Withsome projectionsestimating that AI will boost the GDP of some local economies by 26% by 2030, its easy to see the rationale for the investment and hype.

Among AI researchers and data scientists, one of the major steps in ensuring AI delivers on the promise of enhanced growth and productivity is through expanding the range and capabilities of models available for organizations to use. And top of the agenda is the development, training and deployment of Deep Generative Models (DGMs) which I consider to be some of the most exciting models set for use in industry. But why?

Youve likely already seen the results of a DGM in action theyre actually the same type of AI models that produce deepfakes orimpressionistic art.DGMs have long excited academics and researchers in computer labs, owing to the fact that they bring together two very important techniques that represent the confluence of deep learning and probabilistic modeling: the generative model paradigm and neural networks.

A generative model is one of two major categories of AI models and, as its name suggests, it is a model that can take a dataset and generate new data points based on the input its received so far. This contrasts with the more commonly used and far easier to develop discriminative models, which look at a data point in a dataset and then label or classify it.

The D in DGM refers to the fact that, alongside being generative models, they leverage deep neural networks. Neural networks are computing architectures that give programs the ability to learn new patterns over time what makes a neural network deep is an increased level of complexity offered by multiple hidden layers of inferences between a models input and a models output. This depth gives deep neural networks the ability to operate with extremely complex datasets with many variables at play.

Put together, this means that DGMs are models that can generate new data points based on data fed into them, and that can handle particularly complex datasets and subjects.

As mentioned above, DGMs already have some notable creative and imaginative uses, such as deepfakes or art generation. However, the potential full range of commercial and industrial applications for DGMs is vast and promises to up-end a variety of sectors.

For example, consider the issue of protein folding. Protein folding discovering the 3D structure of proteins allows us to find out which medicines and compounds interact with various types of human tissue, and how. This is essential to drug discovery and medical innovation, but discovering how proteins fold is very difficult, requiring scientists to dissolve and crystallize proteins before analyzing them, which means the whole process for a single protein can last weeks or months. Traditional deep learning models are also insufficient to help tackle the protein folding problem, as their focus is primarily on classifying existing data sets rather than being able to generate outputs of their own.

By contrast, last year the DeepMind teamsAlphaFoldmodel succeeded in reliably being able to anticipate how proteins would fold based solely on data regarding their chemical composition. By being able to generate results in hours or minutes, AlphaFold has the potential to save months of lab work and vastly accelerate research in just about every field of biology.

Were also seeing DGMs emerge in other domains. Last month,DeepMind released AlphaCode, a code-generating AI model thats successfully outperformed the average developer in trials. And the applicability of DGMs can be seen in fields as far-flung as physics, financial modelling, or logistics: through being able to tacitly learn subtle and complex patterns that humans and other deep learning networks are unable to spot, DGMs promise to be able to generate surprising and insightful results in just about every field.

DGMs face some notable technical challenges, such as the difficulty intraining them optimally(especially with limited data sets) and ensuring that they can yieldconsistently accurate outputsin real applications. This is a major driver of the need for further investment to ensure DGMs can be widely deployed in production environments and thus deliver on their economic and social promises.

Beyond the technical hurdles, however, a big challenge for DGMs is in ethics and compliance. Owing to their complexity, the decision-making process for DGMs is very difficult to understand or explain, especially by those who dont understand their architecture or operations. This lack of explainability can create a risk of an AI model developing unjustified or unethical biases without the knowledge of its operators, in turn generating outputs that are inaccurate or discriminatory.

In addition, the fact that DGMs operate on such a layer of high complexity means that theres a risk of it being difficult to reproduce their results. This difficulty with reproducibility can make it hard for researchers, regulators, or the general public to have confidence in the results provided by a model.

Ultimately, to mitigate risks around explainability and reproducibility, devops teams and data scientists looking to leverage DGMs need to ensure theyre using best practices in formatting their models and that they employrecognized explainability toolsin their deployments.

While only just beginning to enter production environments at scale, DGMs represent some of the most promising developments in the AI world. Ultimately, through being able to look at some of the most subtle and fundamental patterns in society and nature, these models will prove transformative in just about every industry. And despite the challenges of ensuring compliance and transparency, theres every reason to be optimistic and excited about the future DGMs promise for technology, our economy and society as a whole.

Rick Hao is lead deep tech partner at pan-European VCSpeedinvest.

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even considercontributing an articleof your own!

Read More From DataDecisionMakers

Read the original post:
Deep generative models could offer the most promising developments in AI - VentureBeat

Molecular Modelling Market research report 2022 offers driving factors, competitive landscape, revenue share analysis, and challenges of the industry has been analysed in the report.

Final Report will add the analysis of the impact of COVID-19 on this industry

Molecular Modelling Market report provides a detailed analysis of global market size, segmentation market growth, industry share, competitive landscape, sales analysis, value chain optimization. Also, the Molecular Modelling market includes market size growth rate analysis by type, regional and country-level market size, impact of domestic, global market key players, trade regulations, strategic market growth analysis, and technological innovations.

Get a Sample Copy of the Report http://www.industryresearch.co/enquiry/request-sample/14235642

About Molecular Modelling Market:

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies.Molecular modelling methods are now used routinely to investigate the structure, dynamics, surface properties, and thermodynamics of inorganic, biological, and polymeric systems. The types of biological activity that have been investigated using molecular modelling include protein folding, enzyme catalysis, protein stability, conformational changes associated with biomolecular function, and molecular recognition of proteins, DNA, and membrane complexes.In 2018, the global Molecular Modelling market size was million USD and it is expected to reach million USD by the end of 2025, with a CAGR during 2019-2025.

List of Top Key Players in Molecular Modelling Market Report Are:

This market study covers the global and regional market with an in-depth analysis of the overall growth prospects in the market. Furthermore, it sheds light on the comprehensive competitive landscape of the global market.

For More Information or Query or Customization Before Buying, Visit at http://www.industryresearch.co/enquiry/pre-order-enquiry/14235642

Global Molecular Modelling Market: Segment Analysis

The research report includes specific segments by region (country), by company, by Type and by Application. This study provides information about the sales and revenue during the historic and forecasted period of 2019 to 2025. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

Molecular Modelling Market by Applications:

Molecular Modelling Market by Types:

Molecular Modelling Market report provides comprehensive analysis of

Get a Sample Copy of the Report http://www.industryresearch.co/enquiry/request-sample/14235642

Some of the Key Questions Answered in this Report:

Geographical Regions covered in Molecular Modelling market report are:

Purchase this Report (Price 3900 USD for a Single-User License) http://www.industryresearch.co/purchase/14235642

Molecular Modelling Market TOC Covers the Following Points:

1 Molecular Modelling Market Overview

1.1 Product Overview and Scope of Molecular Modelling

1.2 Segment by Type

1.2.1 Global Sales Growth Rate Comparison by Type (2021-2025)

1.3 Segment by Application

1.4 Global Market Size Estimates and Forecasts

1.4.1 Global Revenue 2015-2025

1.4.2 Global Sales 2015-2025

1.4.3 Market Size by Region: 2020 Versus 2025

1.5 Industry

1.6 Market Trends

2 Global Molecular Modelling Market Competition by Manufacturers

2.1 Global Sales Market Share by Manufacturers (2015-2020)

2.2 Global Revenue Share by Manufacturers (2015-2020)

2.3 Global Average Price by Manufacturers (2015-2020)

2.4 Manufacturers Manufacturing Sites, Area Served, Product Type

2.5 Market Competitive Situation and Trends

2.5.1 Market Concentration Rate

2.5.2 Global Top 5 and Top 10 Players Market Share by Revenue

2.5.3 Market Share by Company Type (Tier 1, Tier 2 and Tier 3)

2.6 Manufacturers Mergers and Acquisitions, Expansion Plans

2.7 Primary Interviews with Key Players (Opinion Leaders)

3 Molecular Modelling Retrospective Market Scenario by Region

3.1 Global Retrospective Market Scenario in Sales by Region: 2015-2020

3.2 Global Retrospective Market Scenario in Revenue by Region: 2015-2020

3.3 North America Market Facts and Figures by Country

3.3.1 North America Sales by Country

3.3.2 North America Sales by Country

3.3.3 U.S.

3.3.4 Canada

3.4 Europe Market Facts and Figures by Country

3.4.1 Europe Sales by Country

3.4.2 Europe Sales by Country

3.4.3 Germany

3.4.4 France

3.4.5 U.K.

3.4.6 Italy

3.4.7 Russia

3.5 Asia Pacific Market Facts and Figures by Region

3.5.1 Asia Pacific Sales by Region

3.5.2 Asia Pacific Sales by Region

3.5.3 China

3.5.4 Japan

3.5.5 South Korea

3.5.6 India

3.5.7 Australia

3.5.8 Taiwan

3.5.9 Indonesia

3.5.10 Thailand

3.5.11 Malaysia

3.5.12 Philippines

3.5.13 Vietnam

3.6 Latin America Market Facts and Figures by Country

3.6.1 Latin America Sales by Country

3.6.2 Latin America Sales by Country

3.6.3 Mexico

3.6.3 Brazil

3.6.3 Argentina

3.7 Middle East and Africa Market Facts and Figures by Country

3.7.1 Middle East and Africa Sales by Country

3.7.2 Middle East and Africa Sales by Country

3.7.3 Turkey

3.7.4 Saudi Arabia

3.7.5 UAE

4 Global Historic Market Analysis by Type

5 Global Historic Market Analysis by Application

6 Company Profiles and Key Figures in Molecular Modelling Business

7 Manufacturing Cost Analysis

7.1 Key Raw Materials Analysis

7.1.1 Key Raw Materials

7.1.2 Key Raw Materials Price Trend

7.1.3 Key Suppliers of Raw Materials

7.2 Proportion of Manufacturing Cost Structure

7.3 Manufacturing Process Analysis of Molecular Modelling

7.4 Molecular Modelling Industrial Chain Analysis

8 Marketing Channel, Distributors and Customers

8.1 Marketing Channel

8.2 Distributors List

8.3 Customers

9 Market Dynamics

9.1 Market Trends

Read the original post:
Molecular Modelling Market Share 2022 Competitive Analysis of Size, Industry Challenges, Top Manufacturers, Types, Applications and Forecast to 2025 -...

Covid although causing considerable distress but has also allowed innovation in healthcare to be greatly accelerated. The regulatory framework has helped fast track new ideas and solutions. Telecare and total triage have helped patients access their doctors remotely and at their convenience improving access. Telemedicine consultations have increased by over 5000% and changed the doctor-patient interaction. Vaccine development had taken 10 -15years prior to the pandemic and has been shortened to less than a year with rapid design and delivery of clinical trials. Covid supplies were delivered to remote areas using drones. Accurate real-time vital sign measurements, including heart rate and oxygen saturation could be measured via an app using AI-powered light signal processing technology to convert light reflected from blood vessels in the face. Smart stethoscopes were developed that both listen to patients hearts and transmit images of the lungs. Virtual reality was introduced to support training. Robots were introduced to sterilise rooms and offer support to patients. The key to this has been collaboration with industry and healthcare providers coming together at the time of need to share different skill sets. The pandemic is a global problem and the world has responded by sharing global solutions and hopefully this allows the new normal to help springboard health into the future.

Exponential technologies and the digital transformation of health

The world is at an inflection point with exponential technologies converging to help reimagine and reinvent healthcare. Technologies like AI, Robotics, 3D Printing, Gene sequencing/editing, immersive technologies, 5G connectivity, remote sensors, nanobiotechnology offer interlocking building blocks to help prevent illness, monitor physiology, improve diagnosis and offer new treatment modalities. There have been 3 windows to humanity. Copernicus discovered the telescope as a window into the universe. The microscope has helped us understand the intricacies of the human body. Data, the third window is now helping understand and build a personalised approach to medicine. Patients with chronic diseases will be managed more remotely with access to their data and therefore empowering them to be involved in their health. Rare diseases are now being sequences and whole genomic data become available on a national scale which will help understand population health. Drones are delivering medical equipment across cities and 3d printing is allowing the planning of treatment and implant of devices. Eventually this may lead to the production of a bioengineered organs. 5G with its low latency and fast bandwidth will help with remote surgery across continents and smart ambulances bringing valuable support to the roadside. The development of chatbots, digital twins and digital humans will ask important questions on what the future interaction between a patient and doctor may look like.

The Future of medical education

Medical education is going through a paradigm shift as technology enhanced learning are enabling hybrid models of delivering quality education. We have moved from papyrus to books to eLearning/online platforms. More recently tele platforms have become the normal. The pandemic has forced new ways of delivering clinical medicine. The future will encompass more traditional models converted to using augmented, virtual and mixed reality. Students will be trained more remotely with classrooms replaced by virtual rooms. Anatomy dissection will be enhanced digitally, clinical ward rounds can be supported by mixed reality and surgical operations will be viewed in virtual reality with more interactivity allowing teachers to be hundreds of miles away or even on the other side of the world. This will allow the democratisation of education that will allow every student to access world class education breaking down the barriers of cost and location. The adage of see one do one, teach one will be superseded by simulation using CGI or real time images thereby supporting training of practical procedures. Digital Health, entrepreneurship and innovation needs to be at the heart of any new curriculum to produce the digital doctor of tomorrow.

Education 4.0 will allow clinical teaching to use all of the available platforms for a richer experience.

The Metaverse

In October 2017, I entered and embraced the metaverse for the first time from my operating theatre from the Royal London Hospital. I had created my avatar and joined surgeons from London, India and the US. We were able to interact in the virtual world across three continents and three time zones simultaneously. We shared assets like CT scans, X-rays and 3d models to discuss the patient during live surgery. Imagine a world where a surgeon could call on help when required by simply transitioning from the real to the virtual world, allowing the true democratisation of healthcare.

In his 1992 book Snow Crash, American author Neal Stephenson introduced the term metaverse, referring to the 3D visual space occupied by lifelike human avatars. People in this dystopian future wear realistic mirrors to connect with the digital world! In Silicon Valley, it is a well-known novel. Metaverse is an all-encompassing 3D visual space shared by everyone. The metaverse can be thought of as a cluster of connected earths, just as the visible universe is a group of planets connected to space. Can the metaverse become a part of the healthcare system of the future?

Digital Surgery

Surgery is moving rapidly from analogue to a digital interface. The new 5 pillars of digital surgery are connected care, robotics, data analytics/AI, surgical navigation and remote collaboration. Over the last 20 years we have seen enhanced visualisation and improved fine dexterity allowing surgeons to manipulate a robot with the potential improvement in outcomes for the patients. The robot wars in 2022 are allowing more access and flexibility in offering minimal access surgery. By having large amounts of real time data during an operation by using analytics will also aid surgeons to assess their performance and move away from subjective methods of performance to a more robust and transparent process. For decades surgical acumen has been only facilitated by personal judgement. The use of artificial intelligence and computer vision will allow surgical navigation during a procedure allowing surgeons an intraoperative map to help avoid damage to important structures and ensuring cancers can be removed in their entirety to allow a potential cure. Preoperative planning will be supported by mixed reality headsets to overlay images on the patient to improve accuracy and decision making. Remote telemonitoring will be possible due to the power of connectivity with 5G allowing surgeons across the world to support global training and improve standards.

AI in healthcare

Artificial intelligence and deep machine learning is changing the face of medicine and may be the most important technology to be integrated into healthcare. AI has promised hope which has been followed by much hype. We are now witnessing the reality of real time applications of AI. AI can enhance clinical productivity due to its ability to handle a large capacity of tasks that are well suited for automation. AI can reduce the burden of clerical work of doctors thus improving the quality of care and allow them to spend more time with patients and the healthcare team

The collection of good quality data is a pre requisite for an algorithm to produce meaningful conclusions and outputs. This data includes patient biometrics, natural language and operational data. The outcomes have been to improve early detection/triage, diagnosis, disease management and monitoring diseases and wellbeing. Analysis of images has been well researched with AI algorithms diagnosing abnormalities on chest xrays and CT scans with over 90% accuracy. Dermatology is being supported by smart AI to help diagnose skin cancers. Drug development has been accelerated by molecular identification and targeting. Protein folding has been unravelled using powerful AI. Chatbots and intelligent conversation engines are helping with triage and stratifying risk.

However, the ethics need to be considered as there also inherent risks of AI with false prediction and the inappropriate use of patient data. This needs careful debate and patient engagement to ensure that AI is safely and responsible implemented.

See original here:
How healthcare has been transformed post covid by technology - Medium

Arthur Horwich, MD, Sterling Professor of Genetics and professor of pediatrics, has received the 2022 Human Frontier Science Program (HFSP) Nakasone Award in recognition of his work uncovering a molecular machine and its mechanism of mediating protein folding in the cell. He is sharing the prize with his colleague Franz-Ulrich Hartl, MD, director of the Max Planck Institute of Biochemistry in Germany.

The HFSP Nakasone Award is designated for scientists whose work has led to significant breakthroughs in the life sciences. It was named after Japans former Prime Minister Yasuhiro Nakasone, who believed in the importance of international collaboration for the advancement of science. Since the establishment of the program in 1989, more than 4,500 awards have been given to over 7,500 researchers worldwide.

The prize represents a recognition that our work is a major step towards understanding the process of how information is transferred in the cell, says Horwich. The recipients of this award are all distinguished investigators, and it is humbling to join that group.

Although Horwich trained as a physician at Brown University, he also always found himself drawn to the lab. As a postdoctoral fellow at Yale School of Medicine, he grew curious about the biology behind how proteins within a cell move into membrane-bound organelles like mitochondria. This became the subject of his research after he established his own lab at Yale in the 1980s.

In the mid 1980s, two scientists had shown that proteins needed to be unfolded to pass through the two mitochondrial membranes into the innermost matrix compartment. Once a protein reached the matrix space, they believed it would then spontaneously refold on its own to its active form. Horwich, however, hypothesized that there may be machinery that assists the folding of imported proteins.

We proposed that under normal conditions, the machine would mediate the folding of the newly-imported proteins like pieces of spaghetti into its characteristic three dimensional structure, Horwich says. That was considered to be a heretical idea back then.

Horwichs work led to the discovery of a double-ringed macromolecule in the mitochondria, now called the chaperonin, required for protein folding. In mutant cells that lacked this structure, proteins would misfold inside the mitochondria and aggregate into clumps, similar to a process occurring in many neurodegenerative diseases. Over the next two decades, Horwich studied the newly discovered machineryfound also in the cytosol of all cells where it assists folding of newly made proteinsto better understand its mechanism.

The chaperonin, he found, has a hydrophobic [water-fearing] greasy surface at the inside of its rings. A protein, on the other hand, has a hydrophilic [water-loving] exterior and hydrophobic core. Yet when a protein is unfolded, its hydrophobic core is exposed. The chaperonin, he says, works like fly paper to capture an unfolded protein inside a ring. After this binding step, the machine attaches a lid-like structure (co-chaperonin) to the ring, housing the unfolded protein. In doing so, it releases the protein from the wall of the ring into the now-encapsulated chamber, where it can fold in solitary confinement without any chance of aggregating with other proteins, reaching its functional form. A final step of hydrolysis of ATP by the machine releases the lid and the folded protein from the machine like a jack-in-the-box.

I had bumped into something that was against the dogma of the field, and I had only been an independent investigator for a year or two, says Horwich. It was scary to make that proposition that protein folding was assisted rather than spontaneous.

Horwichs research was made possible through close collaboration with Hartl and his extensive expertise at the biochemistry level. The two have won numerous awards for their work, including the prestigious $3 million Breakthrough Prize 2020 and the Dr. Paul Janssen Award for Biomedical Research in 2019. Their joint efforts, says Horwich, embody the spirit of their most recent award. Prime Minister Nakasone felt that science could be significantly promoted by having investigators from different countries getting together to fulfill collaborative experiments, he says.

Studying protein folding, he continues, has significant implications. Neuroscientists have shown that protein misfolding can lead to neuronal injury and cell death, and it is closely associated with neurological diseases including Alzheimers, Parkinsons and amyotrophic lateral sclerosis (ALS). Horwichs lab has been studying protein misfolding in mouse models of ALS to better understand how to change the course of the disease.

Mother nature put together a beautiful, 7-fold symmetric folding machine, says Horwich. The biologist in me loves the notionreally, what was a privilegeof getting to learn more about this unbelievable structure.

Link:
Nakasone Prize Won By Arthur Horwich, MD < Yale School of Medicine - Yale School of Medicine

The news: Seattle-based protein engineering company Cyrus Biotechnology has raised $18 million and acquired Orthogonal Biologics, a spinout from the University of Illinois at Urbana Champaign, which is developing a COVID-19 therapeutic.

Combining forces: Cyrus has built up a software-and-screening platform to re-design natural proteins, leveraging tech spun out of the University of Washingtons Institute for Protein Design. IPDs tools to predict protein folding have been a boon to Cyrus, which was co-founded in 2015 by former IPD postdoc Lucas Nivon. The company recently inked a deal with immune biotech Selecta Biosciences worth up to $1.5 billion and has worked with more than 90 other industry partners, including pharma giant Janssen.

The new acquisition brings on board Orthogonals platform for deep mutational scanning, a method that can assess up to one million mutant versions of a protein in a single experiment. Orthogonal also adds two new protein-based therapeutics to Cyrus pipeline, including a potential COVID-19 drug.

Counteracting COVID-19: Orthogonals COVID-19 agents are built to resemble ACE2, the human protein that the COVID-19 virus uses to enter human cells. The agents are designed to act as decoys, binding to the virus and disarming it.

Why it matters: Drug companies are fast leveraging IPDs recently-released RoseTTAfold and another powerful tool to predict protein folding developed by DeepMind, AlphaFold. Plugging in different tech and drug pipelines, such as those developed by Orthogonal, promises to accelerate the development of new therapeutics. Cyrus recently brought on RoseTTAfold, building on its use of an earlier IPD tool, called Rosetta.

Cyrus has proven the power of its Rosetta-based platform as a software and services company. We are very excited to now apply those software and laboratory tools directly for Cyruss partners and in house drug discovery, said Geeta Vemuri, founder and managing partner at Agent Capital, in a statement.

The field is growing rapidly. Alphabet, for instance, in November launchedIsomorphic Labs to build off of DeepMinds protein folding research.

The backers: Investors in the new deal include OrbiMed Advisors, Trinity Ventures, Agent Capital, Yard Ventures, Washington Research Foundation (WRF), iSelect Fund, W Fund, family offices, and individual investors. Selecta Bioscience is a strategic investor in the Series B round, which brings total funding to date to $28.9 million, including $8 million in venture funding raised in 2017. Terms of the acquisition were not disclosed.

Whats next: The cash will be used to move Cyrus labs from a temporary space atAlexandria LaunchLabstoa buildingnear the Seattle waterfront that houses Universal Cells and other biotechs. Cyrus will also partner with contract research organizations for preclinical testing of the the COVID-19 agent and other therapies.

The small Orthogonal team has moved to Seattle, including COOKui K. Chanand CEOErik Procko, a University of Illinois professor of biophysics and quantitative biology, now on leave. Both are former senior fellows in the lab of David Baker, IPD head. Cyrus will continue relationships with key University of Illinois researchers, including professorsJalees RehmanandAsrar B. Malik, who are performing studies in animals. Cyrus is hiring protein biochemists and senior leadership in drug discovery, aiming to grow from 25 to 30 employees in the next six months.

By merging our company with Cyrus we can create a unified biologics discovery platform, said Procko.

More deals:

Koch Investments Group invests $100 million in Vancouver, B.C.-based Standard Lithium.Standard Lithium is testing the commercial viability of extracting lithium, a key component of electric batteries, at a 150,000-acre location in Arkansas. The company has commissioned a demonstration plant to extract the metal. It also has 45,000 acres of mineral leases in the Mojave Desert in San Bernardino County, Calif.

Barn2Door raises $6 million to advance software that connects farmers to customers. Seattle-based Barn2Door serves thousands of farmers across the U.S., helping them sell food directly to consumers with e-commerce software that manages sales, inventory, logistics, and more. The new funding brings total funding to $17.6 million to date, building on a $6 million round in August, 2020. The latest funding was led by Quiet Capital, with participation from existing major investors Bullpen Capital, lead Edge Capital, RAINE Ventures, Sugar Mountain Capital, as well as new investors Serra Ventures and Navigate Ventures.

Vancouver, B.C.-based Spare Labs raises $18M for mobility software. Spare Labs provides software for public transit, ride-sharing and other shared transportation. It will use the funding to enable better cooperation between different transportation providers. The Series A round was led by Inovia Capital with participation from Kensington Capital, Link VC, Ramen VC, Ridge Ventures, TransLink Capital and Japan Airlines (as JAL Innovation Fund) and Nicola Wealth, amongst others.

Editors note: This story has been updated to include Cyrus future plans.

Original post:
Funding news: Cyrus lands $18M and buys startup developing COVID-19 therapeutic; Spare Labs snags $18M for mobility software - GeekWire