AlphaFold Developers Win US$3-million for Solving Protein Folding

The researchers behind the AlphaFold artificial-intelligence (AI) system have won one of this year’s US$3-million Breakthrough prizes — the most lucrative awards in science. Demis Hassabis and John Jumper, both at DeepMind in London, were recognized for creating the tool that has predicted the 3D structures of almost every known protein on the planet.

“Few discoveries so dramatically alter a field, so rapidly,” says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City. “It’s really changed the practice of structural biology, both computational and experimental.”

Since DeepMind released an open-source version of AlphaFold in July 20211, more than half a million researchers have used the machine-learning system, generating thousands of papers. In July this year, DeepMind released 200 million protein structures predicted from amino-acid sequences. So far, the data have been harnessed to tackle problems ranging from antibiotic resistance to crop resilience.

Other Prizes

Life-sciences Breakthrough prize was awarded jointly to sleep scientists Masashi Yanagisawa at the University of Tsukuba, Japan, and Emmanuel Mignot at Stanford University in Palo Alto, California, for independently discovering that narcolepsy is caused by a deficiency of the brain chemical orexin.

Quantum pioneers
This year’s Breakthrough Prize in Fundamental Physics is shared between four founders of the field of quantum information: Peter Shor at the Massachusetts Institute of Technology in Cambridge; David Deutsch at the University of Oxford, UK; Charles Bennett at IBM in Yorktown, New York; and Gilles Brassard at the University of Montreal in Canada. Their research laid the groundwork for the development of ultra-secure communications and computers that might one day outperform standard machines at some tasks.

Clifford Brangwynne at Princeton University in New Jersey and Anthony Hyman at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, won a prize discovering a mechanism by which cell contents can organize themselves by segregating into droplets.

Math Prize

The Breakthrough Prize in Mathematics goes to Daniel Spielman, a mathematician at Yale University in New Haven, Connecticut. Spielman was recognized for multiple advances, including the development of error-correcting codes to filter out noise in high-definition television broadcasts.

5 thoughts on “AlphaFold Developers Win US$3-million for Solving Protein Folding”

    • Knowing how a protein folds tells a molecular biologist exactly how it behaves chemically.That is the second part of the genome puzzle; once we are able to read the base pair sequences, we still had to figure out what those proteins which were code in each locus did. This is what AlphaFold 2 does.

      In fact, the article itself shows that the software has been used to advance problems like antibiotic resistance of bacteria: by elucidating the shape of the proteins those bacteria use to resist antibiotics, the spacific mechanism which causes the antibiotic resistance is understood, which allows researchers to select or design molecules which bypass the mechanism and thus the resistance.

      • “Knowing how a protein folds tells a molecular biologist exactly how it behaves chemically.”

        Eh… I’d say you are being far too generous to say that.

        More realistically: Knowing how a protein folds provides the next piece of the puzzle when a molecular biologist is trying to work out how the protein interacts with all the other molecules in a living creature.

        We are still a long way from knowing exactly how it behaves.

        But there is another application: in nanotech it would be really good to be able to be able to make billions of identical complex 3D shapes with atomic precision.

        Well, you can do this with proteins: encode some DNA to make a protein, use PCR to replicate the DNA a billion times, then feed the DNA the right precursor chemicals and it will make a billion copies of that protein. And NOW we can predict how that protein will fold up to make a complex 3D shape.

        So the reverse system can work too. Design some nanotech that needs a complex 3D shaped molecule, reverse the calculations to work out the required protein, and make the appropriate DNA.

        • Protein catalytic behavior comes down to shape. Knowing how it folds can help us determine what shape will result, and thus what catalytic properties it may have.

        • Ok, perhaps “exactly” is too strong a word, but still, like sanman says, the behaviour of an enzyme is essentially down to its geometry, or more precisely, the spatial location of its charge concentrations or “actve sites.”

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