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Category : Protein Folding

A protein puzzle game called Foldit turns up 99 promising ways to confound coronavirus – GeekWire

This is one of the high-scoring protein designs that will be turned into an actual protein binder for testing as an coronavirus-blocking agent. (Stomjoh via Foldit / UW Institute for Protein Design)

Who would have thought a video game could identify potential treatments for COVID-19? Researchers at the University of Washingtons Institute for Protein Design certainly thought so, and so far the game has produced 99 chances to win.

The game is a protein-folding puzzler called Foldit, which was created at UWs Center for Game Science more than a decade ago and has attracted nearly more than 750,000 registered players since then.

Foldits fans find ways to twist virtual protein structures into all sorts of contortions. Some of those contortions turn out to have therapeutic value, which can raise a players score in the game. And that can have real-world implications for countering the coronavirus.

On the cellular level, protein structures can switch on biological processes, or act as keys to spring open the locks that protect cells from harm. For example, the coronavirus that causes COVID-19, known as SARS-Cov-2, has a spike-like protein structure thats particularly well-shaped for unlocking a cells defenses and getting inside to do its dirty work.

Once researchers mapped the virus shape, the Institute for Protein Design set up a challenge for Foldits players. They were tasked with folding virtual proteins into shapes that could latch onto the coronavirus skeleton key and gum it up, rendering it useless for a cellular break-in.

Thousands of designs were submitted and scored over the course of three rounds of competition. Now the institutes researchers have selected 99 designs, 33 from each round, that will be turned into real-world proteins known as binders for testing as antiviral agents.

It will be a few more weeks before genes arrive and we can begin experiments on the Foldit designs, Brian Koepnick, a UW biochemist who focuses on Foldit, told players in blog post. In the meantime, well continue to work on designing better binders in Foldit.

In an earlier blog post, Koepnick cautioned players that the synthetic proteins dont always work as well in the real world as they do in Foldits computer-generated chemistry lab.

Protein binder design is a very hard problem one at the forefront of computational biology and there are other physical factors that are difficult to account for, he wrote. Even if our metrics look good on paper or on a computer, only laboratory testing will tell us whether these designer proteins actually fold and bind to the target.

But if the institute can turn one of the 99 designs into a workable drug that can stop coronavirus in its tracks, Foldit players wont be the only winners.

To get in on the game, head on over to the Foldit website, download the software and follow the instructions. After you get a feel for the game by playing the tutorials, check out this 49-minute video for tips on tackling the coronavirus puzzles.

Update for 11:35 p.m. PT April 1: Weve updated some outdated figures for the number of registered Foldit players.

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A protein puzzle game called Foldit turns up 99 promising ways to confound coronavirus - GeekWire

Recommendation and review posted by Alexandra Lee Anderson

Q&A: Markus Buehler on setting coronavirus and AI-inspired proteins to music – MIT News

The proteins that make up all living things are alive with music. Just ask Markus Buehler: The musician and MIT professor develops artificial intelligence models to design new proteins, sometimes by translating them into sound. His goal is to create new biological materials for sustainable, non-toxic applications. In a project with theMIT-IBM Watson AI Lab, Buehler is searching for a protein to extend the shelf-life of perishable food. In anew studyin Extreme Mechanics Letters, he and his colleagues offer a promising candidate: a silk protein made by honeybees for use in hive building.

Inanother recent study, in APL Bioengineering, he went a step further and used AI discover an entirely new protein. As both studies went to print, the Covid-19 outbreak was surging in the United States, and Buehler turned his attention to the spike protein of SARS-CoV-2, the appendage that makes the novel coronavirus so contagious. He and his colleagues are trying to unpack its vibrational properties through molecular-based sound spectra, which could hold one key to stopping the virus. Buehler recently sat down to discuss the art and science of his work.

Q:Your work focuses on the alpha helix proteins found in skin and hair. Why makes this protein so intriguing?

A: Proteins are the bricks and mortar that make up our cells, organs, and body. Alpha helix proteins are especially important. Their spring-like structure gives them elasticity and resilience, which is why skin, hair, feathers, hooves, and even cell membranes are so durable. But theyre not just tough mechanically, they have built-in antimicrobial properties. With IBM, were trying to harness this biochemical trait to create a protein coating that can slow the spoilage of quick-to-rot foods like strawberries.

Q:How did you enlist AI to produce this silk protein?

A:We trained a deep learning model on the Protein Data Bank, which contains the amino acid sequences and three-dimensional shapes of about 120,000 proteins. We then fed the model a snippet of an amino acid chain for honeybee silk and asked it to predict the proteins shape, atom-by-atom. We validated our work by synthesizing the protein for the first time in a lab a first step toward developing a thin antimicrobial, structurally-durable coating that can be applied to food. My colleague,Benedetto Marelli, specializes in this part of the process. We also used the platform to predict the structure of proteins that dont yet exist in nature. Thats how we designed our entirely new protein in the APL Bioengineering study.

Q: How does your model improve on other protein prediction methods?

A: We use end-to-end prediction. The model builds the proteins structure directly from its sequence, translating amino acid patterns into three-dimensional geometries. Its like translating a set of IKEA instructions into a built bookshelf, minus the frustration. Through this approach, the model effectively learns how to build a protein from the protein itself, via the language of its amino acids. Remarkably, our method can accurately predict protein structure without a template. It outperforms other folding methods and is significantly faster than physics-based modeling. Because the Protein Data Bank is limited to proteins found in nature, we needed a way to visualize new structures to make new proteins from scratch.

Q: How could the model be used to design an actual protein?

A: We can build atom-by-atom models for sequences found in nature that havent yet been studied, as we did in the APL Bioengineering study using a different method. We can visualize the proteins structure and use other computational methods to assess its function by analyzing its stablity and the other proteins it binds to in cells. Our model could be used in drug design or to interfere with protein-mediated biochemical pathways in infectious disease.

Q:Whats the benefit of translating proteins into sound?

A: Our brains are great at processing sound! In one sweep, our ears pick up all of its hierarchical features: pitch, timbre, volume, melody, rhythm, and chords. We would need a high-powered microscope to see the equivalent detail in an image, and we could never see it all at once. Sound is such an elegant way to access the information stored in a protein.

Typically, sound is made from vibrating a material, like a guitar string, and music is made by arranging sounds in hierarchical patterns. With AI we can combine these concepts, and use molecular vibrations and neural networks to construct new musical forms. Weve been working on methods to turn protein structures into audible representations, and translate these representations into new materials.

Q: What can the sonification of SARS-CoV-2's "spike" protein tell us?

A: Its protein spikecontains three protein chains folded into an intriguing pattern. These structures are too small for the eye to see, but they can be heard. We represented the physical protein structure, with its entangled chains, as interwoven melodies that form a multi-layered composition. The spike proteins amino acid sequence, its secondary structure patterns, and its intricate three-dimensional folds are all featured. The resulting piece is a form of counterpoint music, in which notes are played against notes. Like a symphony, the musical patterns reflect the proteins intersecting geometry realized by materializing its DNA code.

Q: What did you learn?

A: The virus has an uncanny ability to deceive and exploit the host for its own multiplication. Its genome hijacks the host cells protein manufacturing machinery, and forces it to replicate the viral genome and produce viral proteins to make new viruses. As you listen, you may be surprised by the pleasant, even relaxing, tone of the music. But it tricks our ear in the same way the virus tricks our cells. Its an invader disguised as a friendly visitor. Through music, we can see the SARS-CoV-2 spike from a new angle, and appreciate the urgent need to learn the language of proteins.

Q: Can any of this address Covid-19, and the virus that causes it?

A:In the longer term, yes. Translating proteins into sound gives scientists another tool to understand and design proteins. Even a small mutation can limit or enhance the pathogenic power of SARS-CoV-2. Through sonification, we can also compare the biochemical processes of its spike protein with previous coronaviruses, like SARS or MERS.

In the music we created, we analyzed the vibrational structure of the spike protein that infects the host. Understanding these vibrational patterns is critical for drug design and much more. Vibrations may change as temperatures warm, for example, and they may also tell us why the SARS-CoV-2 spike gravitates toward human cells more than other viruses. Were exploring these questions in current, ongoing research with my graduate students.

We might also use a compositional approach to design drugs to attack the virus. We could search for a new protein that matches the melody and rhythm of an antibody capable of binding to the spike protein, interfering with its ability to infect.

Q: How can music aid protein design?

A: You can think of music as an algorithmic reflection of structure. Bachs Goldberg Variations, for example, are a brilliant realization of counterpoint, a principle weve also found in proteins. We can now hear this concept as nature composed it, and compare it to ideas in our imagination, or use AI to speak the language of protein design and let it imagine new structures. We believe that the analysis of sound and music can help us understand the material world better. Artistic expression is, after all, just a model of the world within us and around us.

Co-authors of the study in Extreme Mechanics Letters are: Zhao Qin, Hui Sun, Eugene Lim and Benedetto Marelli at MIT; and Lingfei Wu, Siyu Huo, Tengfei Ma and Pin-Yu Chen at IBM Research. Co-author of the study in APL Bioengineering is Chi-Hua Yu. Buehlers sonification work is supported by MITs Center for Art, Science and Technology (CAST) and the Mellon Foundation.

Q&A: Markus Buehler on setting coronavirus and AI-inspired proteins to music - MIT News

Recommendation and review posted by Alexandra Lee Anderson

How a supercomputer network of 700,000 PCs is helping to find a Covid-19 cure – NS Tech

The race to find a coronavirus vaccine is on, with about 35 companies and academic institutions across the world working feverishly on the case. But Sars-CoV-2, the virus that causes Covid-19, is a novel, as well as a large and complex structure. The process of discovering a vaccine is complemented and accelerated by building a solid ground layer in knowledge about the virus. One of the projects helping to plug the gaps in our understanding is Folding@home, based at Stanford University. Its a distributed computing project that links up the machines of citizen scientists across the world willing to donate excess computing resources from their devices to help run simulations of disease proteins at scale.

For the past 20 years, the project has been mapping disease proteins involved in Alzheimers and cancer, but in late February it began modelling the protein structures of Covid-19 too. This decision prompted the projects biggest ever spike in new volunteers signing up via downloadable software around 600,000 so far, putting it on track to reach one million total users. The network is now operating at an exaflop of computing power: 1,000,000,000,000,000,000 (a billion billion) operations per second.

Historically, vaccines contain enfeebled versions of the virus that trigger specific antibodies priming the human bodys immune system to react effectively to the real thing. But in the case of Covid-19, most research groups around the world are developing newer recombinant nucleic acid vaccines that contain scraps of the virus genetic code (DNA or RNA).

The ball was set rolling in mid-January when Chinese scientists published the full genome of the Covid-19 virus (all 29,903 nucleic bases). Scientists are able to use this information to single out sets of genes that correspond to specific proteins that make up the building blocks of the virus form essential information to formulating a vaccine. But this is only the beginning.

The proteins of Covid-19 are constantly shuffling and rearranging in response to their environment, and its these dynamical motions that Folding@homes molecular simulations are attempting to map. In a nutshell, this means simulating in a computer how each atom in a very large biomolecule wiggles and jiggles over time, says Vincent Voelz, associate professor in theory and computation at Temple University and a member of Folding@home. These movements indicate how the virus functions. As Voelz puts it, Covid-19 proteins are the nanoscale machines that the virus tricks an infected cell into making so it can propagate.

Of particular interest to Folding@home, and research groups investigating Covid-19 more generally, is the S-protein making up the spikes on the virus outer shell, that it uses to gain access to human cells. Folding@home has created a simulation of the spike protein, that is composed of three interlocking proteins, and a pocket that helps the virus bind to human cells and infect them.

The point of mapping proteins is to find out which parts of proteins the immune system might target, says Jim Naismith, professor of structural biology at the University of Oxford. In Covid-19, the spike protein is a particularly popular binding spot for human antibodies, meaning it could be key to developing an effective vaccine. Scientists are mapping all those epitopes [protein segments] where people are mounting good responses to them, and then theyll test those antibodies in trials, says Naismith.

Running computations to produce simulations of this type of biological puzzle is time and energy-intensive. Folding@homes distributed network of computers is able to run calculations with greater speed and efficiency than any single computer could. In effect, large calculations are broken down into smaller ones that are run concurrently on thousands of displaced machines. The power of Folding@homes distributed network is not directly comparable to one supercomputer, because the system is not operating as a single unit on a single problem. But if it was, it would be faster. The fastest supercomputers available today operate at a scale of hundreds of petaflops between a third and a half of the speed of an exaflop.

Folding@home isnt the only project directing vast quantities of computing power towards understanding Covid-19. In the US, a partnership including the US government, IBM, and others has began to grant promising Covid-19 projects access to 16 supercomputers. Summit, the worlds most powerful non-distributed computer system in the world, was tasked with identifying compounds that would be effective in binding to the spike proteins of the Covid-19 organism, thereby preventing the attachment of the virus to host cells. It came up with 77 matches.

Beyond brute computing force, artificial intelligence is also playing an increasingly important role in virus modelling. Traditionally, experiments to determine the structure have taken months or longer. But computational methods can provide a much speedier way to predict protein structures from amino acids sequences. In cases where the structure of a similar protein has already been experimentally defined, algorithms based on template modelling can provide accurate predictions of the protein structure. Googles DeepMind recently announced AlphaFold, a deep learning system that focuses on predicting protein structure accurately when no structures of similar proteins are available, called free modelling.

While Folding@homes work is not pitched directly at creating a vaccine, its useful for modern computational drug discovery, which relies on sampling the many possible conformations of the proteins, and modelling how drug molecules might bind to them. At present, there are not good experimental techniques that can probe these motions at the atomic scale that can be achieved with computational modelling, says Voelz.

Computational mapping complements structural mapping of the virus using laboratory techniques such as cryogenic electron microscopy. What you can do with computing is, if possible, use evolutionarily related proteins that we already know something about the architecture and the active site and then build a computing model using those, says Tom Blundell, biochemist and structural biologist at Cambridge University.

Folding@home has been able to go one further. Voelzs group at Temple University are partnering with researchers at the Diamond Light Source in the UK who have done groundbreaking work in solving over a thousand different crystal structures of the coronavirus main protease, and have discovered several drug fragments that bind to sites on the protein. Based on these initial fragment screening results, the computing power of Folding@home is being used to virtually screen a huge number of potential drug compounds including those from the COVID Moonshot project to prioritise which to synthesise and experimentally test.

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How a supercomputer network of 700,000 PCs is helping to find a Covid-19 cure - NS Tech

Recommendation and review posted by Alexandra Lee Anderson

Coronavirus pushes Folding@Homes crowdsourced molecular science to exaflop levels – TechCrunch

The long-running Folding@Home program to crowdsource the enormously complex task of solving molecular interactions has hit a major milestone as thousands of new users sign up to put their computers to work. The network now comprises an exaflop of computing power: 1,000,000,000,000,000,000 operations per second.

Folding@Home started some 20 years ago as a way then novel, and pioneered by the now-hibernating SETI@Home to break up computation-heavy problems and parcel them out to individuals for execution. It amounts to a crude supercomputer distributed over the globe, and while its not as effective as a real supercomputer in blasting through calculations, it can make short work of complex problems.

The problem in question being addressed by this tool (administrated by a group at Washington University in St. Louis) is that of protein folding. Proteins are one of the many chemical structures that make our biology work, and they range from small, relatively well-understood molecules to truly enormous ones.

The thing about proteins is that they change their shape depending on the conditions temperature, pH, the presence or absence of other molecules. This change in shape is often what makes them useful for example, a kinesin protein changes shape like a pair of legs taking steps to carry a payload across a cell. Another protein like an ion channel will open to let charged atoms through only if another protein is present, which fits into it like a key in a lock.

Image Credits: Voelz et al.

Some such changes, or convolutions, are well-documented, but most by far are totally unknown. But through robust simulation of the molecules and their surroundings we can discover new information about proteins that may lead to important discoveries. For example, what if you could show that once that ion channel is open, another protein could lock it that way for longer than usual, or close it quickly? Finding those kinds of opportunities is what this sort of molecular science is all about.

Unfortunately its also extremely computation-expensive. These inter- and intra-molecular interactions are the kind of thing supercomputers can grind away at endlessly to cover every possibility. Twenty years ago supercomputers were a lot rarer than they are today, so Folding@Home started as a way to do this sort of heavy computing load without buying a $500 million Cray setup.

The program has been chugging along the whole time, and likely got a boost when SETI@Home recommended it as an alternative to its many users. But the coronavirus crisis has made the idea of contributing ones resources to a greater cause highly attractive, and as such there has been a huge increase in users so much so that the servers are struggling to get problems out to everyones computers to solve.

Examples of COVID-19-related proteins as visualized by Folding@Home.

The milestone its celebrating is the achievement of an exaflop of processing power, which is I believe a sextillion (a billion billion) operations per second. An operation is a logical operation, like AND or NOR, and several of them together form mathematical expressions, which eventually add up to useful stuff like saying at temperatures above 38 degrees Celsius this protein deforms to allow a drug to bind at this site and disable it.

Exascale computing is the next goal of supercomputers; Intel and Cray are building exascale computers for the National Laboratories that are expected to come online in the next couple of years but the fastest supercomputers available today operate at a scale of hundreds of petaflops, or about half to a third the speed as an exaflop.

Naturally these two things are not directly comparable Folding@Home is marshaling an exaflops worth of computing power, but it is not operating as a single unit working on a single problem, as the exascale systems are built to. The exa- label is there to give a sense of scale.

Will this kind of analysis lead to coronavirus treatments? Perhaps later, but almost certainly not in the immediate future. Proteomics is basic research in that it is at heart about better understanding the world around (and within) us period.

COVID-19 (like Parkinsons, Alzheimers, ALS and others) isnt a single problem, but a large, poorly bounded set of unknowns; its proteome and related interactions are part of that set. The point isnt to stumble onto a magic bullet but to lay a foundation for understanding so that when we are evaluating potential solutions, we can pick the right one even 1% faster because we know that this molecule in that situation acts like so.

As the project noted in a blog post announcing the release of coronavirus-related work:

This initial wave of projects focuses on better understanding how these coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

If you want to help, you can download the Folding@Home client and donate your spare CPU and GPU cycles to the cause.

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Coronavirus pushes Folding@Homes crowdsourced molecular science to exaflop levels - TechCrunch

Recommendation and review posted by Alexandra Lee Anderson

What is Folding@home and how can we use it to fight the Coronavirus? – Pocket-lint

In modern times, the advent of more intelligent computing technology means that processing power can be used to help with scientific research.

That research involves using simulations to analyse the make-up of proteins in the human body and how they "fold".

Misfolding proteins are often the cause for diseases likes Alzheimers, Parkinson's, various types of cancer, ALS and more.

Using technology to research these proteins allows scientists to more efficiently and more quickly develop drugs to help combat the issues.

This is particularly relevant at the moment with the Coronavirus pandemic.

The good news is, you can help with this and doing so is really simple too. All you need to do is get involved with Folding@home.

Folding@home is a distributed computing project run by Stanford University. The aim of the project is to examine how proteins fold and it does this using spare computing power.

We first wrote about folding@home in 2007, but with rising concern about coronavirus - and confirmation of the project's involvement in researching COVID-19 - now is a great time to revisit this project and lend some support.

The idea behind the project is around shared computing power.

Lots of people have computers and a lot of the time those computers aren't doing anything - they're just sitting around with spare computational power. Folding@home takes advantage of that spare power to put it to a good cause - researching various diseases.

It's a very technical thing - both in terms of how a distributed computing project works and investigating folding proteins, but fortunately, you don't have to understand either of those things to lend your support, because it all happens in the background.

The idea is that when there are millions of computers doing a little bit of work in the background, the project will have greater computational power at its disposal, which is a great benefit to researchers.

To help, you just need to download the software to your computer and set it to run. The program then downloads "work units" and processes them to send the data back.

Generally, you'll still find you're able to use your computer as you normally would without any hassle, but while you work, play, stream or browse, you'll be helping fight disease.

All you have to do is head over to the folding@home website and you can download the software for whatever platform you're on. You'll install a small programme that will connect to the back to the project and then start churning data.

You can download Folding@home for Windows, Mac or Linux machines, so whatever you're using it's easy to get started.

It's also free to download, so it'll cost you nothing to do your part.

There are detailed guides on how to install the Folding@homesoftware for Windows, Mac and Linuxon the site too.

The installation process is really simple though. Download the software, install it, set up an identity and start folding.

You can open Folding@homein a browser to see how you're doing. You also have the option to adjust how much processing power the software is using. If you're not using your computer you could set it to "full" to do the most work or "light" if you're doing something more intensive and need to dial back the folding for a bit.

The benefits of Folding@homeare fairly straightforward. With very little technical knowledge you can set up your computer to help find cures for disease.

The more people that get involved, the more processing power there is to simulate the protein folding and the faster the results will be achieved.

This system also means that the organisation doesn't need to pay for supercomputers as everyone around the world is lending a hand.

When running Folding@home,it is possible to choose a project. This means you can dedicate your processing power to support fighting a particular disease. You can choose from Alzheimer's, Cancer, Huntington's, Parkinson's or any disease.

There is no current way to select Coronavirus as a disease to fight, but the team has said selecting any disease will still help with the research into the pandemic.

When you starting using the software you'll see you'll slowly accumulate points. These points are designed to encourage friendly competition between you, your friends and other people online.

Points are calculated based on the work units you complete and the points vary depending on the complexity of those work units. Some of the work involves studying small proteins, others are on more complex proteins and so the points awarded will varying depending on that.

You can also join a team in order to help climb a stats ladder to compete for the position of the best team. The stats of the teams are viewable here. Though you don't need to join a team and can fold anonymously if you'd prefer.

If you'd rather be part of a group effort, you can join a team easily from the web control interface that opens in a browser.

Under "I'm folding as" you'll find a link to "change identity". If you click that you'll see a pop-up that lets you choose a name and a team.

To join a team you need to know the team's number. You can find the team numbers from the stats page.

Alternatively, you can create your own team by filling out this simple form. Once you've set your team up, make a note of the number and get your friends to join in too to help do their part.

Folding@home is designedto be safe. It's been carefully tested and the servers for it are behind high-security firewalls to keep everything safe and secure. You won't have any problems running this software on your computer.

The folding@home team has confirmed that it is supporting researchers at Memorial Sloan Kettering in New York City to develop treatments for COVID-19. As part of an open science approach, findings are shared with other researchers, with the global goal of developing drugs or therapies to combat the coronavirus.

This video shows the Folding@homesimulations of the COVID-19 protein. It's this sort of simulation that helps researchers understand what's happening with the proteins and how they're infecting human cells.

That data could then be used to develop ways to block the virus in the first place.

The worldwide issues with COVID-19 has lead to more and more people using Folding@home. That, in turn, has lead to a massive increase in processing power for the project. The project has now broken theexaFLOP barrier meaning it's more powerful than even the most powerful supercomputer. This also means it's carrying out over1,000,000,000,000,000,000 operations per second.

Dr Greg Bowman has recently revealed that the number of people folding has reached almost five times the amount as the number before the pandemic outbreak.

What are you waiting for? Download the software and do your bit too.

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What is Folding@home and how can we use it to fight the Coronavirus? - Pocket-lint

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Folding@home Taking the Coronavirus Battle to ExaFLOP Levels – Screen Rant

Folding@home is a distributed computing project used to simulate protein folding. The project, which is available for free to anyone, asks users to install software on a computer so it can leverage the user's processing power to calculate data. It's something like Bitcoin mining except it can save lives. In simple terms, the data itself involves calculating all the ways a protein molecule could move over time because that movement impacts the shape of the molecule, and that shape has a strong bearing on how the molecule will ultimately function. Charting these movements requires a staggering number of calculations (literally billions), so Folding@home users are taking on some of that workload. The information gleaned from these movements can then be used by biologists to determine how things like viruses will function, and how to treat them. For more information on ways to get involved, check the following article.

Related:Here's How You (& Your Computer) Can Help Fight Coronavirus

The Folding@home project has been used in cancer research going back to the advent of broadband internet access, and it has also aided in research on Ebola: it recently led to the discovery of an unexpected way to treat Ebola with drugs, which was previously considered impossible. Now that Folding@home has been unleashed on COVID-19, its creators have pushed for the many PC owners spending time at home due to social distancing to step up and lend their computing resources to the project. Step up they have, as the Folding@home Twitter account recently revealed the project has "crossed the exaFLOP barrier". This feat of computing means they've reached enough computing power to calculate 1,000,000,000,000,000,000 (that's one quintillion) floating-point operations per second, which, according to a reply on Twitter, is more power than exists in the top 103 supercomputers on Earth combined.

This is a staggering feat considering the immense power required to do large-scale FLOP calculations for those familiar with the term "teraflop", an exaflop is one million teraflops. It's also a great sign of comradery amongst us Earthlings. Folding@home is an easy process to start, it runs relatively unobtrusively, and it can even be a little fun, but it's still a surprise to see so many people taking the effort to help with the project.

Throughout this process, tech enthusiasts and tech brands in the PC space have taken strides to promote Folding@home and get more people on board. Companies like Nvidia are sharing Folding@home leaderboards to drive friendly competition and acknowledge individual efforts. The combination of this new social media word of mouth and people's strong desire to help end the coronavirus are the primary factors for the project's recent explosion of popularity, and hopefully, this trend continues. Way to go, humans.

Next:How Apple & Facebook Were Able to Source Masks During Coronavirus

Source:Folding@home/ Twitter

Star Wars: How Lor San Tekka Knows Kylo Ren's Identity In The Force Awakens

Hubert has been a journalist in spirit since age six, and can't see any good reasons to argue with that, so here we are. He spends most of his days working to leave the world a better place than it was when he showed up and trying to be better at Street Fighter.

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Folding@home Taking the Coronavirus Battle to ExaFLOP Levels - Screen Rant

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Petrobras directing supercomputer capacity to Folding@home Project effort on coronavirus – Green Car Congress

Brazil-based oil company Petrobras will direct part of the processing capacity of its high-performance computers (HPC) to contribute to the Folding@home Project effort on studying the coronavirus behavior in the human body and how the disease progresses, from the interaction of viral proteins, making way for for the development of medication and vaccines.

Launched in 2000, the Folding@home project is a distributed computing project for simulating protein dynamics, including the process of protein folding and the movements of proteins implicated in a variety of diseases. It brings together citizen scientists who volunteer to run simulations of protein dynamics on their personal computers.

Insights from this data are helping scientists to better understand biology, and providing new opportunities for developing therapeutics. Among other advancements, this project has already helped in identifying the protein which links the SARS-CoV-2 betacoronavirus (the virus that causes COVID-19) to human cells.

Up to two supercomputers in Petrobras service may have their processing capacity redirected to this research: the Santos Dumont, Latin Americas largest supercomputer, located in the National Scientific Computing Lab (Laboratrio Nacional de Computao Cientfica - LNCC), in Petrpolis (RJ), which recently had its capacity enhanced by collaboration with another lab, the company and its partners in the Libra Consortium; and OBGON, result of the partnership with Senai-Cimatec, installed in Salvador (BA).

For the initiative, the company will mobilize 60% of Santos Dumonts capacity2 petaflops (equivalent to the computational capacity of 2 million laptops)in addition to 50% of Senai-Cimatec capacity, corresponding to one petaflop (1 million laptops).

The use of these supercomputers allows for accelerating the simulation time in order for researchers to achieve results faster in their research.

In addition to this initiative, Petrobras will mobilize its high performance computational resources for research projects of Brazilian universities in fighting coronavirus. One of the potential projects, in a partnership with both PUC-Rio and Senai-Cimatec, is the use of artificial intelligence techniques (deep learning) in order to help differentiate the X-ray exam of a regular flu patient and the X-ray exam of a coronavirus patient.

The algorithms create repetition patterns and, by comparing the data, it is possible to arrive at a diagnosis. It is a test cheaper and faster than, for example, tomography and PCR blood exams.

These initiatives integrate a broad front led by Petrobras, which is mobilizing its professionals from various fields of knowledge that may contribute in fighting the coronavirus, in partnership with universities, companies, social organizations, Brazilian and foreign institutions. Its goal is to propose solutions that may use the companys technological structure, equipment and technical consulting in order to aid the effort in fighting the pandemic, in the prevention, treatment and hospital support fronts.

In the same way, Petrobras is also dedicated to initiatives such as donation supply to institutionsincluding, for example, safety and hygiene items to the UFRJ hospitaland mobilizing its structures for storage, among others.

On the Folding@home Project. Viruses have proteins that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, researchers want to understand how these viral proteins work and how to design therapeutics to stop them.

Folding@homes specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means.

Taking the experimental structures as starting points, Folding@home can simulate how all the atoms in the protein move, effectively filling in the rest that experiments miss. Doing so can reveal new therapeutic opportunities.

In a recent paper, Folding@home simulated a protein from Ebola virus that is typically considered undruggable because the snapshots from experiments dont have obvious druggable sites. But the simulations uncovered an alternative structure that does have a druggable site. Experiments confirmed the computational prediction, and now there is a search for drugs that bind this newly discovered binding site.

Folding@home seeks to do the same thing with SARS-CoV-2. On 10 March, after initial quality control and limited testing phases, the Folding@home team released an initial wave of projects simulating potentially druggable protein targets from SARS-CoV-2 and the related SARS-CoV virus (for which more structural data is available) into full production on Folding@home.

SARS-CoV-2 RBD domain in complex with human ACE2 receptor (PDBID: 6vsb, 6acg) [10.1126/science.abb2507, 10.1371/journal.ppat.1007236]

This initial wave of projects focuses on better understanding how these coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

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Petrobras directing supercomputer capacity to Folding@home Project effort on coronavirus - Green Car Congress

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NVIDIA (NASDAQ: NVDA) Upgraded Over the Growing Deployment of Distributed Computing for Medical Applications to Counter the Coronavirus (COVID-19)…

NVIDIA (NASDAQ:NVDA) has been upgraded by the analysts at Needham to a Buy from the previous Hold recommendation. The stocks price target has also been increased to $270, marking a 10.36 percent upside potential from current levels.

According to the investment note penned by Needham analyst Rajvindra Gill today, investors should head for companies with "superior balance sheets and robust free cash flow" during the current environment characterized by acute macro uncertainty.

COVID-19 Pandemic Accelerating Re-Shoring

Mr. Gill saw the increasing need for GPUs in distributed computing medical applications amid the ongoing coronavirus (COVID-19) pandemic as a key bullish factor behind the upgrade.

As a refresher, projects such as Folding@home utilize distributed computing power to simulate and analyze the process of protein folding along with the diseases and complications that arise from protein misfolding and aggregation.

Before discussing implications for NVIDIA, additional context may be beneficial to our readers. Proteins are essentially complex chains of amino acids that perform a variety of functions in an organisms body from acting as building blocks of bones and muscles to stimulants for biochemical reactions (enzymes). While scientists have sequenced the human genome, it is not very helpful in trying to analyze the precise manner in which a particular protein functions. This is where a folding analysis comes handy. Folding is simply the manner in which a protein folds, adopting a particular shape in the process. This shape or fold then determines the function that a specific protein performs (of course a proteins constituents are also considered in this analysis).

These findings, in turn, assist medical professionals in developing new drugs to counter a myriad of diseases, including Alzheimers disease, Huntingtons disease, cystic fibrosis, BSE (Mad Cow disease), etc. With the advent of the coronavirus pandemic, these distributed computing projects have received an added impetus as thousands of users from around the world have banded together to loan their computing power to medical experts who are trying to counter this pandemic, indirectly driving a short-term boost in GPU demand to NVIDIAs benefit. In fact, Wccftech has been an enthusiastic partner in this endeavor.

It should be noted though that these distributed computing projects run calculations on millions of PCs in order to simulate protein folding. For this reason, the success of Folding@home and other similar projects is quite hard to match as data centers take time to build and are generally not scalable. Scalability is an essential requirement for these tasks as the computing power is only required for a maximum of 2 to 3 months for a specific project. Therefore, any boost that NVIDIA receives will likely only be transitory.

Facebook (NASDAQ: FB) Stock Price Target Slashed by Cowen INC. (NASDAQ: COWN) as the Social Media Giant Reveals a Slump in Ad Revenue Amid the Economic Slowdown

Of course, this is not the only upgrade that NVIDIA has received in recent days. In an investment note published on the 12th of March, Morgan Stanley (NYSE:MS) named the company one of its top ideas in the semiconductor space. According to the Wall Street behemoth, the recent selloff has made NVIDIAs current valuation attractive.

The analyst Joseph Moore wrote:

For larger cap growth with the best chance of powering through tough conditions, we favor Nvidia.

Moreover, a recovery in cloud spending and the expected launch of NVIDIAs 7nm Ampere in the second half of 2020 provided additional impetus for Morgan Stanleys upgrade.

Interestingly, Moore pointed to the growing need for remote computing amid the coronavirus pandemic as a bullish factor for NVIDIA.

NVIDIAs stock has declined by 9.61 percent year to date (based on Mondays closing price). For comparison, the NASDAQ Composite has declined by 18.87 percent in the same timeframe. The stock is currently trading at $244.65, up by 15.33 percent (as of 10:48 a.m. ET).

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NVIDIA (NASDAQ: NVDA) Upgraded Over the Growing Deployment of Distributed Computing for Medical Applications to Counter the Coronavirus (COVID-19)...

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Crusoe Energy Systems Is Donating Computing Resources to Coronavirus Vaccine Research and Discovery Efforts – Yahoo Finance

Wasted Natural Gas To Power The Fight Against COVID-19

Crusoe Energy Systems, Inc. has deployed more than twenty energy-intensive computing modules throughout Americas oil and gas fields as part of its Digital Flare Mitigation system, which captures otherwise flared or wasted natural gas to power computing processes at the wellhead. Today the company announces that it has begun allocating a portion of its computing systems to the search for a coronavirus vaccine.

Crusoe is working with the Folding@Home Consortium, a distributed computing system for life-science research launched out of Stanford University. The Consortium allows researchers to remotely utilize Crusoes computational resources for the vaccine search and discovery process, and recently launched a new protein folding simulation project specifically targeting vaccines and therapeutic antibodies for COVID-19.

Crusoe has configured eight of its most advanced graphic processing units to support the Consortiums vaccine development project, and commenced work units for COVID-19 research in Crusoes field operations center in North Dakota earlier this week. Crusoe is now one of the largest contributors of computing power to the protein folding Consortium, ranking in the top 10% of computational power providers for the vaccine research system. Crusoe ultimately plans to deploy protein folding servers to multiple flare gas-powered computing modules in the oilfield after expanding network bandwidth at selected sites.

COVID-19 is closely related to the SARS coronavirus. Both coronaviruses infect the lungs when viral proteins bind to receptor proteins in lung cells. A SARS therapeutic antibody, which is a protein that can prevent the SARS coronavirus from binding to lung receptors, has been developed previously. To develop a similar antibody for COVID-19, researchers need to better evaluate how the COVID-19 spike protein binds to receptors in the human body. The Consortiums new protein folding project simulates antibody proteins and how they might prevent COVID-19 viral infection, however, the simulation process is very computationally intensive and therefore energy intensive.

Crusoe can support this vaccine research using its distributed computing resources deployed at natural gas flaring sites in Montana, North Dakota, Wyoming and Colorado. Today, Crusoe consumes millions of cubic feet of natural gas per day that would have otherwise been wasted by burning in the air, or "flared." Instead, that waste gas powers Crusoes mobile, modular computing systems, which are deployed directly to the wellhead to mitigate flaring. Crusoes initial computational use case was blockchain processing. More recently the company has been developing high performance and general-purpose cloud computing solutions, which are used in a variety of applications including machine learning, artificial intelligence, and protein folding.

"At this time of growing global concern around the coronavirus, we are grateful to have the opportunity to support the Folding@Home Consortiums search for a vaccine," said Chase Lochmiller, CEO and co-founder of Crusoe. "Weve configured very powerful computing hardware that is typically used for machine learning and artificial intelligence research to search for helpful therapies against coronavirus. This is very much in keeping with Crusoes vision that distributed computing resources have an important role to play in solving real world problems."

Crusoe began processing work units for COVID-19 on March 15th. In addition to COVID-19, the Company has previously completed work units related to cancer research.

About Crusoe Energy Systems Inc.

Crusoe Energy Systems provides innovative solutions for the energy industry. By converting natural gas to energy-intensive computing, Crusoes Digital Flare Mitigation service delivers an environmentally sound way to create a beneficial use for otherwise wasted natural gas. Crusoe currently has flare mitigation projects operating in Wyomings Powder River Basin oilfield, Colorados Denver-Julesburg oilfield and North Dakota and Montanas Bakken oilfield. Systems are scalable up to millions of cubic feet per day and can be deployed anywhere in the United States or Canada.

Background on Flaring

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Natural gas flaring has become an acute pain point for shale oil producers, which produce natural gas as a byproduct of oil. This oil-associated natural gas production has outpaced gas pipeline infrastructure in many parts of the North American shale industry. In the absence of pipeline capacity, operators tend to burn natural gas in a process known as "flaring" or "combusting." Approximately 335 billion cubic feet of natural gas are flared annually in the United States, according to latest 2017 data from the World Banks Global Gas Flaring Reduction Partnership (GGFR), which is enough gas to power more than 7 million U.S. homes. Flaring generates pushback from the public and policymakers, who increasingly raise environmental concerns around resource waste, visual impacts and air quality.

Please reach out to or visit to learn more, and follow Crusoe on Linkedin and Twitter.

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Company Contacts: Chase LochmillerCEO and Chairman

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Organisms grow in wave pattern, similar to ocean circulation – Big Think

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the egg's surface.

These waves are produced by billions of activated proteins that surge through the egg's membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.

Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.

In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an egg's surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.

The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.

"Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems," says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. "It's a manifestation of this very universal wave pattern."

"It opens a completely new perspective," adds Jrn Dunkel, associate professor of mathematics at MIT. "You can borrow a lot of techniques people have developed to study similar patterns in other systems, to learn something about biology."

Fakhri and Dunkel have published their results today in the journal Nature Physics. Their co-authors are Tzer Han Tan, Jinghui Liu, Pearson Miller, and Melis Tekant of MIT.

Previous studies have shown that the fertilization of an egg immediately activates Rho-GTP, a protein within the egg which normally floats around in the cell's cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasm's morass to attach to the egg's membrane, snaking along the wall in waves.

"Imagine if you have a very dirty aquarium, and once a fish swims close to the glass, you can see it," Dunkel explains. "In a similar way, the proteins are somewhere inside the cell, and when they become activated, they attach to the membrane, and you start to see them move."

Fakhri says the waves of proteins moving across the egg's membrane serve, in part, to organize cell division around the cell's core.

"The egg is a huge cell, and these proteins have to work together to find its center, so that the cell knows where to divide and fold, many times over, to form an organism," Fakhri says. "Without these proteins making waves, there would be no cell division."

MIT researchers observe ripples across a newly fertilized egg that are similar to other systems, from ocean and atmospheric circulations to quantum fluids. Courtesy of the researchers.

In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an egg's surface when they altered the protein's concentration.

For their experiments, they obtained about 10 eggs from the ovaries of starfish through a minimally invasive surgical procedure. They introduced a hormone to stimulate maturation, and also injected fluorescent markers to attach to any active forms of Rho-GTP that rose up in response. They then observed each egg through a confocal microscope and watched as billions of the proteins activated and rippled across the egg's surface in response to varying concentrations of the artificial hormonal protein.

"In this way, we created a kaleidoscope of different patterns and looked at their resulting dynamics," Fakhri says.

The researchers first assembled black-and-white videos of each egg, showing the bright waves that traveled over its surface. The brighter a region in a wave, the higher the concentration of Rho-GTP in that particular region. For each video, they compared the brightness, or concentration of protein from pixel to pixel, and used these comparisons to generate an animation of the same wave patterns.

From their videos, the team observed that waves seemed to oscillate outward as tiny, hurricane-like spirals. The researchers traced the origin of each wave to the core of each spiral, which they refer to as a "topological defect." Out of curiosity, they tracked the movement of these defects themselves. They did some statistical analysis to determine how fast certain defects moved across an egg's surface, and how often, and in what configurations the spirals popped up, collided, and disappeared.

In a surprising twist, they found that their statistical results, and the behavior of waves in an egg's surface, were the same as the behavior of waves in other larger and seemingly unrelated systems.

"When you look at the statistics of these defects, it's essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale," Dunkel says. "It's the same universal phenomenon, just scaled down to the level of a cell."

The researchers are particularly interested in the waves' similarity to ideas in quantum computing. Just as the pattern of waves in an egg convey specific signals, in this case of cell division, quantum computing is a field that aims to manipulate atoms in a fluid, in precise patterns, in order to translate information and perform calculations.

"Perhaps now we can borrow ideas from quantum fluids, to build minicomputers from biological cells," Fakhri says. "We expect some differences, but we will try to explore [biological signaling waves] further as a tool for computation."

This research was supported, in part, by the James S. McDonnell Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation.

Reprinted with permission of MIT News. Read the original article.

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