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Velasea Enters the Folding At Home Project to Help Fight Coronavirus/COVID-19
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Velasea Enters the Folding At Home Project to Help Fight Coronavirus/COVID-19
July 15, 2021
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Vida Soleimanzadeh
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With the incredible rise in worldwide COVID-19 cases, multiple research facilities have turned their attention to finding therapeutics and prophylactics for the disease. A great example of this effort is the Folding@Home Consortium, started by a group of University labs.

Folding@home (FAH or F@h) 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 while providing new opportunities for developing therapeutics.

Here at Velasea, we have teamed up with NVidia and are using their DGX platform to contribute to this amazing project. Currently, we are running a dedicated DGX server with four (4) V100 GPUs – capable of processing around 600 work units a week – which is over 15 times that of a standard PC with consumer-grade GPUs. We are currently in the top 10% of computational donors to the program and climbing every day.

The quadV100 water-cooled NVIDIA DGX

So, what’s REALLY going on under the hood?

What we are doing, and how you can help

Proteins are molecular machines that perform many of the functions we associate with life. They sense the environment (e.g. in taste and smell),perform work (e.g. muscle contraction and breaking down food), and play structural roles (e.g. your hair). They are made of a linear chain of chemicals called amino acids that, in many cases, spontaneously “fold” into compact, functional structures. Much like any other machine, it is how a protein’s components are arranged and move that determine the protein’s function. In this case, the components are atoms.

Viruses also have proteins that they use to suppress our immune systems and reproduce themselves.

To help tackle the novel Coronavirus, researchers want to understand how these viral proteins work and how they can design therapeutics to stop them from multiplying.

There are many experimental methods for determining protein structures. While extremely powerful, they only reveal a single snapshot of a protein’s usual shape. However, proteins have many moving parts, so the most important aspect to observe is the protein in action. It’s the structures that they can’t see experimentally which may be the key to discovering a new therapeutic.

Using football as an analogy for the experimental situation, it’s as if you could only see the players lined up for the snap (the single arrangement the players spend the most time in) and were blind to the rest of the game.

Seeing a single structure of a protein (left) is like seeing players lined up for the snap in football. It’s important information, but a lot is missing too. The protein structure shows a sphere for each atom (blue) and red arrows highlighting the one drug-binding site in this protein.

Our contribution to this group effort is in using computer simulations to understand proteins’ moving parts. Watching how the atoms in a protein move relative to one another is vital viewing as it captures valuable information inaccessible by other means.

Taking the experimental structures as starting points, we can simulate how all the atoms in the protein move, effectively filling in the rest of the “game” that experiments miss.

Doing so can reveal new therapeutic opportunities. For example, in a recent paper, researchers simulated a protein from the Ebola virus that is typically considered ‘undruggable’ because the snapshots from experiments don’t have obvious druggable sites. However, eventually, simulations uncovered an alternative structure that does have a druggable site. Most importantly, they then performed experiments that confirmed the computational prediction, and are now searching for drugs that bind to this newly discovered binding site.

The goal is to do the same thing with the novel Coronavirus.

An experimental structure of an Ebola protein doesn’t have obvious druggable sites (no deep pockets among the atoms, shown as spheres). Our simulations captured a motion that creates a potentially druggable site in this Ebola protein. Instead of showing spheres for each atom, this illustration shows a ribbon tracing the linear chain of amino acids (chemicals) that comprise the protein.

Results

As analyses of the simulations are completed, the consortium makes all data publicly available so that others working in the field can view the results. This ensures anyone with ideas for new methods (e.g. the always growing machine learning data analysis) has this knowledge base available at any time.

 https://osf.io/2h6p4/wiki/home/ and https://osf.io/dp4cb/wiki/home/

What YOU can do.

Donate Computing Power

Downloading Folding@home and helping run simulations is the primary way to contribute. These calculations are enormous so every little bit helps! Each simulation is like the equivalent of a lottery ticket. The more tickets purchased, the better the chances of hitting the jackpot. Usually, your computer will never be idle, but we’ve had such an enthusiastic response to our COVID-19 work that you will see some intermittent downtime as we sprint to setup more simulations. Please be patient with us! There is a lot of valuable science to be done, and we’re getting it running as quickly as we can.

If you or someone you know would like to donate your spare CPU and GPU cycles to contribute to solving humanity’s most pressing health crisis, click this link to download the F@H utility. When asked for your team name, you can improve Velasea’s standings by entering “266741” as the TeamID. Otherwise, you can see our current stats on our team page, where we are currently in the top 0.8% of contributors!

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