Category Archives: Protein Folding

The overall theme of Splunk's four-part 2021 Predictions report is the rapid acceleration of digital transformation, driven by the specific event of the COVID-19 pandemic, and the momentum of data technologies that have brought us into a true Data Age. Nowhere is that acceleration going to be more transformative than around the application of artificial intelligence and machine learning.

AI/ML was a hot topic before 2020 disrupted everything, and over the course of the pandemic, adoption has increased. We've seen it particularly in terms of security use cases, but security is far from the only arena. Already, it seems like artificial intelligence is everywhere. John Sabino, our chief customer officer, notes in the report that every software vendor is claiming AI/ML as a secret sauce in its solutions, and there's a danger of fatigue as AI/ML becomes something everyone talks about, but no one ever quite sees.

Despite that, meaningful applications of machine learning in particular are already common. We see machine learning having an impact in everything from how recruiters parse stacks of resumes to how businesses analyze subtle trends in customer behavior; from improving user experience with everything from how web pages are served and products are recommended to intelligent chat features. And developments go far beyond business. Deep learning techniques produced a recent breakthrough in protein folding, which has applications in developing effective medical treatments, using enzymes to break down industrial waste, and more. It represents a considerable advance in AI development.

As we see machine learning adopted by more organizations, for more purposes, there are three innovations that I am keeping an eye out for in the near future:

The Emerging Technology Predictions report goes deeper into these topics, and other AI/ML predictions, including a stellar use case in medical research. It also covers 5G, AR/VR, blockchain and more. These are technologies that are going to reshape our world, and it's fascinating to look ahead even as the future is unfolding.

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Predictions: The AI Challenges of 2021 - Marketscreener.com

The Kottke Ride Home podcast has been humming away since August and host Jackson Bird has been sharing some great stuff lately. From todays show comes this New York magazine piece by David Wallace-Wells about the stunning speed with which the Covid-19 vaccine was developed:

You may be surprised to learn that of the trio of long-awaited coronavirus vaccines, the most promising, Modernas mRNA-1273, which reported a 94.5 percent efficacy rate on November 16, had been designed by January 13. This was just two days after the genetic sequence had been made public in an act of scientific and humanitarian generosity that resulted in Chinas Yong-Zhen Zhangs being temporarily forced out of his lab. In Massachusetts, the Moderna vaccine design took all of one weekend. It was completed before China had even acknowledged that the disease could be transmitted from human to human, more than a week before the first confirmed coronavirus case in the United States. By the time the first American death was announced a month later, the vaccine had already been manufactured and shipped to the National Institutes of Health for the beginning of its Phase I clinical trial.

Mondays show featured the intrigue behind the discovery of a real life treasure:

And if you look back to last week, Jackson clued us in to Radiooooo (The Musical Time Machine), Tetris championships, Chinas Change 5 mission to the Moon, and DeepMinds AI breakthrough in protein folding.

If any or all of that sounds interesting to you, you can subscribe to Kottke Ride Home right here or in your favorite podcast app.

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Recently on the Kottke Ride Home Podcast - kottke.org

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Kevin Magee (Microsoft), Christopher Salvatore (Cybersecure Catalyst), and Tahseen Shabab (Penfield) explore how Canada can build a cybersecurity ecosystem.

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DeepMind said that its system, called AlphaFold, had solved what is known as the protein folding problem.

Gebru is known for coauthoring a groundbreaking paper that showed facial recognition to be less accurate at identifying women and people of color, which means its use can end up discriminating against them.

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Startups raised a total of $349.5 million in Q3 2020, a 135 percent increase for the Waterloo Region.

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A|I: The AI Times Sounding the alarm - BetaKit

Professor Tshilidzi Marwala is the Vice-Chancellor and Principal of the University of Johannesburg. He recently penned an opinion article that first appeared in theDaily Maverickon 07 December 2020.

This year has been a great definer. As we waged a battle against an unknown entity, proponents of artificial intelligence (AI) were swift to act. Just last week, DeepMind announced that it has cracked what is referred to as a 50-year-old scientific riddle. It has solved the protein-folding problem. In other words, it can determine a proteins 3D shape from its amino-acid sequence, making it easier to develop treatments for a range of diseases from cancer to the coronavirus.

To do this, researchers trained the DeepMind algorithm on a public database, which contained about 170,000 protein sequences and their shapes over a few weeks, running the equivalent of 100 to 200 graphics processing units. In recent years, DeepMind has been most recognised for its ability to beat human beings in games such as Go or Atari Classics. These were, in a sense, testing grounds for ultimately solving real-world problems.

As DeepMinds founder Demis Hassabis said at the announcement last week: It marks an exciting moment for the field. These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems. In fact, many had expected this kind of advancement in AI only in a few decades from now.

This indicates the advent of the Fourth Industrial Revolution (4IR) the era we find ourselves in, where intelligent technologies permeate all aspects of our lives. AI, which is the most significant technology of the 4IR, is already changing how we live, work and communicate by reshaping government, education, healthcare and commerce. In his bookDeep Medicine,Eric Topol distinguishes between shallow and deep medicine. Shallow medicine is a healthcare system based on observations of community groups (for example, people of African descent have a higher risk of prostate cancer than other community groups), whereas deep medicine is based on individualised medicine that is enabled by AI.

Not only do we have more access to information than ever before, but we also see a confluence of cyber, physical and biological technologies that no longer exist in labs, but impact on us every day. Proponents have long argued that the 4IR could be the key to finding solutions to some of our most deep-seated problems. The unprecedented responses to the coronavirus pandemic have been an exemplification of this.

For instance, AI has made the detection of the coronavirus easier. Alibabas research institute, Damo Academy, has developed an AI algorithm that can detect the coronavirus in just under 20 seconds with 96% accuracy. The AI was trained using 5,000 samples from confirmed cases and can detect the virus from chest CT scans, differentiating between infected patients and general viral pneumonia cases.

South Korea was swift to act following the outbreak in China, anticipating a spread into its borders. The government organised the private sector to develop testing kits for the virus. Molecular biotech company Seegene in South Korea used AI to accelerate these kits development. This facilitated the submission of its solution to the Korea Centers for Disease Control and Prevention (KCDC) only three weeks after scientists began working on this solution. Under normal situations, this process would have taken two to three months with an approval process of about 18 months.

It is not just pockets of AI that have cropped up in these regions. The opportunity for AI to speed up the implementation of vaccines, drugs and diagnostics is gaining traction elsewhere. Projects such as the Covid-19 Open Research Dataset provide free access to the texts of almost 25,000 research papers, while the Covid-net open access neural network is working on systems similar to those deployed by the Damo Academy.

Companies such as BenevolentAI, based in the United Kingdom, are using AI and the available data to scour through existing drugs that could be used to treat coronavirus patients until a vaccine becomes available.

Vir Biotechnology and Atomwise, start-ups in the United States, are using algorithms to identify a molecule that could facilitate treatment. Now, as various vaccines are in the final testing stages, algorithms are being used to sift through data on potential adverse reactions. Companies such as Genpact UK have signed contracts with the UK government to ensure that nothing is missed as preparations begin for mass vaccinations in the coming year. This is significant given the rapid timeline in which many of these vaccines have been developed and the various unknowns that remain.

AI solutions once thought of as futuristic and unrealistic are now commonplace. We see far more advances than we had expected at this stage, perhaps indicating the urgency that the pandemic has presented.

Similarly, there has been a shift to find AI solutions in Africa. Data science competition platform Zindi which is based in South Africa and Ghana has initiated a competition sponsored by the Artificial Intelligence for Development-Africa Network (AI4D-Africa), which requires data scientists to create an epidemiological model that forecasts the spread of Covid-19 throughout the globe. This is critical for both policy makers and health workers to make informed decisions and take action.

In Kenya, start-up Afya Rekod deploys AI and Blockchain to establish a health-data platform that lets users store their health records, access health information and connect to health service providers.

Of course, it is not only in the context of the coronavirus pandemic that there have been AI advances. There have been great strides in bridging some of the inequalities that exist in the healthcare system. In Rwanda, for instance, the government has collaborated with US start-up Zipline to deliver blood supplies by drones to remote areas. Where a journey would have taken three hours by car, a drone can complete the trip within six minutes. This addresses emergency medical supply requirements in rural areas.

Just last month, to improve access and quality of services to rural communities in South Africa, the Department of Health in Limpopo installed CT-Scans and Picture Archiving Communication System (PACS) in the province. The availability of this equipment at regional hospitals now improves the speed of diagnosis and management of the associated conditions and indicates an embracing of the 4IR.

This is vital because according to the General Household Survey conducted by Statistics SA, only 17% of South Africans have medical insurance, the critical key for private healthcare. About 82% of South Africans fall outside the medical-aid net, and, as a result, are largely dependent on public healthcare. According to Statistics South Africa, in 2017, 81% of households that used public healthcare services were satisfied or very satisfied with public facilities services.

AI also addresses concerns of a shortage of doctors, particularly in the public sector. For example, the increased speed and accuracy of cancer diagnostics through analytics which can characterise tumours and predict therapies has not replaced doctors, but quickened their efforts and given them the space to attend to more patients. Technologies such as AI will decrease the cost of health care globally.

Almost two-thirds of healthcare costs are from non-communicable diseases such as cancer, strokes, heart failure and kidney failure that can be treated more effectively and at less cost if diagnosed early.

For example, in China, a company called Infervision developed AI algorithms that efficiently and accurately read medical images to augment radiologists in diagnosing cancer.

As Dhruv Khullar, a physician at New York-Presbyterian Hospital, said, most fundamentally, it means recognising that humans, not machines, are still responsible for caring for patients. It is our duty to ensure that we are using AI as another tool at our disposal not the other way around.

AI solutions once thought of as futuristic and unrealistic are now commonplace. We see far more advances than we had expected at this stage, perhaps indicating the urgency that the pandemic has presented.

What is clear is that, like many other sectors, health care will be transformed by AI and we need to ready ourselves for these shifts. As Enrico Coiera aptly put it inThe Lancetin 2018, what is the fate of medicine in the time of AI? Our fate is to change.

*The views expressed in the article is that of the author/s and does not necessarily reflect that of the University of Johannesburg.

Source: UJ

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Deep medicine: Artificial intelligence is changing the face of healthcare, daily - Yiba

The proteins in our bodies are easily confused with the proteinin food.There are similarities and links between the two for example, both consist of amino acids.

But, when scientists talk about proteins in biology, they are talking about tiny butcomplex molecules that perform a huge range of functions at a cellular level, keeping us healthy and functioning as a whole.

Scientists will often talk about proteins "folding" and say that when they fold properly, we're OK. The way they fold determines their shape, or 3D structure, and that determines their function.

But, when proteins fail to fold properly, they malfunction, leaving us susceptible to potentially life-threatening conditions.

We don't fully understand why: why proteins fold and how, and why it doesn't always work out.

When proteins go wrong: 'Lewy bodies' or protein deposits in neurons can lead to Parkinson's cisease

The whole thing has been bugging biologists for 50 or 60 years, with three questions summarized as the "protein-folding problem."

It appears that that final question has now been answered, at least in part.

An artificial intelligence systemknown as AlphaFold can apparently predict the structure of proteins.

AlphaFold is a descendant of AlphaGo a gaming AI that beat human GO champion Lee Sedol in 2016. GO is a game like chess but tougher to the power of 10.

DeepMind,the company behind AlphaFold, is calling it a "major scientific advance."

To be fair, it's not the first time that scientists have reported they have used computer modeling to predict the structure of proteins;they have done that for a decade or more.

Perhaps it's the scale that AI brings to the field the ability to do more, faster. DeepMind say they hope to sequence the human proteome soon, the same way that scientists sequenced the human genome and gave us all our knowledge about DNA.

But why do it? What is it about proteins that makes them so important for life?

Well, predicting protein structure may help scientists predict your health for instance, the kinds of cancer you may or may not be at risk of developing.

Proteins are indeed vital for life they are like mechanical components, such ascogs in a watch or strings and keys in a piano.

Proteins form when amino acids connect in a chain. And that chain "folds" into a 3D structure. When it fails to fold, it forms a veritable mess a sticky lump of dysfunctional nothing.

Proteins can lend strength to muscle cells, or form neurons in the brain.The US National Institutes of Health lists five main groups of proteins and their functions:

There can be between 20,000 and 100,000 unique types of proteins within a human cell. They form out of an average of 300 amino acids, sometimes referred to as protein building blocks. Each is a mix of the 22 differentknown amino acids.

Those amino acids are chained together, and the sequence, or order, of that chain determines how the protein folds upon itselfand, ultimately, its function.

Protein-folding can be a process of hit-and-miss. It's a four-part process that usually begins with twobasic folds.

Healthy proteins depend on a specific sequence of amino acids and how the molecule 'folds' and coils

First, parts of a protein chain coil up into what areknown as "alpha helices."

Then, other parts or regions of the protein form "beta sheets," which look a bit like the improvised paper fans we make on a hot summer's day.

In steps three and four, you get more complex shapes. The two basic structures combine into tubes and other shapes that resemble propellers, horseshoes or jelly rolls. And that gives them their function.

Tube or tunnel-like proteins, for instance, can act as an express route for traffic to flow in and out of cells. There are "coiled coils" that move like snakes to enable a function in DNA clearly, it takes all types in the human body.

Successful protein folding depends on a number of things, such as temperature, sufficient space in a celland, it is said, even electrical and magnetic fields.

Temperature and acidity (pH values) in a cell, for instance, can affect the stability of a protein its ability to hold its shape and therefore perform its correct function.

Chaperone proteins can assist other proteins while folding and help mitigate bad folding. But it doesn't always work.

Misfolded proteins are thought to contribute to a range of neurological diseases, including Alzheimer's, Parkinson's andHuntington's diseaseand ALS.

It's thought that when a protein fails to foldand perform a specific function, known as "loss of function," that specific job just doesn't get done.

As a result, cells can get tired for instance, when a protein isn't there to give them the energy they need and eventually they get sick.

Researchers have been trying to understand why some proteins misfold more than others, why chaperones sometimes fail to help, and why exactly misfolded proteins cause the diseases they are believed to cause.

Who knows? DeepMind's AlphaFold may help scientists answer these questions a lot faster now. Or throw up even more questions to answer.

Bugs can be tasty. So why is it that we don't we eat more of them? There are plenty of reasons to do so: insects are easy to raise and consume fewer resources than cows, sheep or pigs. They dont need pastures, they multiply quickly and they don't produce greenhouse gasses.

Water bugs, scorpions, cockroaches - on a stick or fried to accompany beer: these are delicacies in Asia, and healthy ones at that. Insects, especially larvae, are an energy and protein bomb. One hundred grams of termites, for example, have 610 calories - more than chocolate! Add to that 38 grams of protein and 46 grams of fat.

Insects are full of unsaturated fatty acids, iron, vitamins and minerals says the UNS Food and Agriculture Organisation (FAO). The organization wants to increase the popularity of insect recipes around the world.

In many countries around the world, insects have long been a popular treat, especially in parts of Asia, Africa and Latin America. Mopane caterpillars, like the ones shown here, are a delicacy in southern Africa. They're typically boiled, roasted or grilled.

Even international fine cuisine features insects. And in Mexican restaurants, worms with guacamole are a popular snack. Meanwhile, new restaurants in Germany are starting to pop up that offer grasshoppers, meal worms and caterpillars to foodies with a taste for adventure.

In Europe and America, beetles, grubs, locusts and other creepy crawlers are usually met with a yuck! The thought of eating deep-fried tarantulas, a popular treat in Cambodia, is met with great disgust. But is there a good reason for that response?

Fine food specialists Terre Exotique (Exotic Earth) offer a grilled grasshopper snack. The French company currently sells the crunchy critters online via special order. A 30-gram jar goes for $11.50 (9 euros).

There are about 1,000 edible insect varieties in the world. Bees are one of them. They're a sustainable source of nutrition, full of protein and vitamins - and tasty for the most part. The world needs to discover this delicacy, says the UN's Food and Agriculture Organization.

In 2012, researchers used ecological criteria to monitor mealworm production at an insect farm in the Netherlands. The result? For the production of one kilogram of edible protein, worm farms use less energy and much less space than dairy or beef farms.

Even in Germany, insects used to be eaten in abundance. May beetle soup was popular until the mid-1900s. The taste has been described as reminiscent of crab soup. In addition, beetles were sugared or candied, then sold in pastry shops.

French start-up Ynsect is cooking up plans to offer ground up mealworms as a cost-effective feed for animals like fish, chicken and pigs. This could benefit the European market, where 70 percent of animal feed is imported.

Author: Lori Herber

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What are proteins and why do they fold? - DW (English)

Every budding biologist learns about proteins and the amino acids that build them. Proteins are the building blocks of life, but knowing the sequence for the protein is only half of the story. How the protein folds onto itself determines what sections are exposed and can interact with other molecules, and therefore also what sections are hidden.

This is called the protein folding problemand has stumped the scientific community for about 50 years. Scores of researchers around the world are working to predict how proteins are folded, many using artificial intelligence (AI).

Biologists want to be able to predict how a protein folds because that gives insight into what it does and how it functions in the body. Geneticists and researchers have gained understanding about genes that encode for proteins, but experts have less knowledge about what happens when proteins are released to do their jobs.

One group at DeepMind, a Google AI offshoot, built an AI system that has done what others have not been able to. The group entered their algorithm, called AlphaFold, in the biennial protein-structure prediction challenge called Critical Assessment of Structure Prediction (CASP). The organizers of CASP look at the accuracy of predictions to assess how good the solutions are. The assessment is done blind, meaning the assessors dont know whose results they are looking at.

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This year, AlphaFold has come out on top, beating its past performance and others in the competition.

This is a big deal,said John Moult, who is a computational biologist at the University of Maryland in College Park and co-founded CASP in 1994, to Nature. In some sense the problem is solved.

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Research groups that dont use AI usually focus on experiments and collect data like X-ray diffraction data. One group that was trying to figure out a bacteria protein has been studying it for a decade while AlphaFold solved it in half an hour, according to Nature.

This is a problem that I was beginning to think would not get solved in my lifetime,said Janet Thornton, who is a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute and a past assessor for CASP, to Nature.

DeepMind is mostly known for its success in chess, Go and other games. Demis Hassabis, DeepMinds founder and chief executive,said to The Guardian, These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems.

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Has Google's DeepMind revolutionized biology? | TheHill - The Hill