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A two-decade-old effort to predict protein folding based on an amino acid sequence using a network of computers and gaming consoles has crossed a threshold of computational power known as the exaFLOP barrier, project organizers announced on Twitter March 25. This is due in part to a burst in users in recent months responding to the need to understand SARS-CoV-2, the virus behind the coronavirus pandemic. The project, Folding@Home, can now perform more than 1,000,000,000,000,000,000 operations per second—multiple times more than the world’s most powerful supercomputer.
Folding@Home began when then–Stanford University chemistry professor Vijay Pande began to enlist users of computers and gaming consoles to add their machines to a network that enabled them to perform computations for the project when not otherwise in use. Over the years, the crowdsourced endeavor has led to 223 publications on protein structures, according to the project’s website,...
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The number of Folding@Home participants surged from 30,000 in February of this year to 400,000 in March, and has since increased by a further 300,000, Ars Technica reports, and it now has a peak performance of 1.5 exaFLOPS, making it seven times faster than the world’s most powerful supercomputer.
A top current priority for Folding@Home is discerning the dynamics of proteins encoded by SARS-CoV-2. That includes the workings of its spike protein, which binds to the ACE2 receptor in human cells to initiate infection.
“It is well established that the spike must undergo a dramatic opening motion to reveal the interface that ultimately binds a human cell. Understanding how the spike opens up . . . could be extremely useful. Every step along the way could potentially be targeted with therapeutics,” Greg Bowman, a professor at Washington University in St. Louis and a former student of Pande who’s involved in the project, tells The Guardian.
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“I hope people will stick around once we get this virus under control and help us get control over diseases like cancer and Ebola,” Bowman tells Ars Technica. “We’re looking to learn why this is so much more infectious. For the foreseeable future, I think we will be quite busy with this.”
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