from the you-gotta-know-when-to-hold-'em-and-when-to-fold-'em dept.
DeepMind has predicted the structure of almost every protein so far catalogued by science, cracking one of the grand challenges of biology in just 18 months thanks to an artificial intelligence called AlphaFold. Researchers say that the work has already led to advances in combating malaria, antibiotic resistance and plastic waste, and could speed up the discovery of new drugs.
Determining the crumpled shapes of proteins based on their sequences of constituent amino acids has been a persistent problem for decades in biology. Some of these amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to accurately determine.
UK-based AI company DeepMind first announced it had developed a method to accurately predict the structure of folded proteins in late 2020, and by the middle of it 2021 it had revealed that it had mapped 98.5 per cent of the proteins used within the human body.
Today, the company announced that it is publishing the structures of more than 200 million proteins – nearly all of those catalogued on the globally recognised repository of protein research, UniProt.
[...] Demis Hassabis, CEO of DeepMind, says that the database makes finding a protein structure – which previously often took years – "almost as easy as doing a Google search". DeepMind is owned by Alphabet, Google's parent company.
[...] While the tool is often, even usually, extremely accurate, its structures are always predictions rather than explicitly calculated results. Nor has AlphaFold yet solved the complex interactions between proteins, or even made a dent in a small subset of structures, known as intrinsically disordered proteins, that seem to have unstable and unpredictable folding patterns.
"Once you discover one thing, then there are more problems thrown up," says Willison. "It's quite terrifying actually, how complicated biology is."
[...] Pushmeet Kohli, who leads DeepMind's scientific team, says that the company isn't done with proteins yet and is working to improve the accuracy and capabilities of AlphaFold.
"We know the static structure of proteins, but that's not where the game ends," he says. 'We want to understand how these proteins behave, what their dynamics are, how they interact with other proteins. Then there's the other area of genomics where we want to understand how the recipe of life translates into which proteins are created, when are they created and the working of a cell."
(Score: 2, Informative) by Anonymous Coward on Saturday July 30 2022, @09:24PM (3 children)
Did Folding@home just get BTFO'd?
(Score: 3, Interesting) by HammeredGlass on Sunday July 31 2022, @05:44PM
I'm glad I gave up on that stuff when SETI@Home got rid of their fun visual representation of the fast fourier transform,etc. A lot of wasted crowd sourced computing just went up in a cloud of carbon heavy electricity.
(Score: 2) by bzipitidoo on Monday August 01 2022, @01:39AM
Probably not. Keep in mind that reporters are always over-dramatizing things. Take a deep breath. Then go read the rather long list of caveats, limitations, disclaimers, and additional angles.
(Score: 3, Informative) by Anonymous Coward on Monday August 01 2022, @07:26AM
no:
1. results obtained with folding at home were used when training alphafold.
2. alphafold does not guarantee correctness, it just very quickly gives a result, that can in turn be used to guide subsequent experiments/calculations. i.e. this database can be used by alphafold as "initial guesses".
(Score: 4, Insightful) by vux984 on Saturday July 30 2022, @10:09PM
predict(inputProtein): shape {
return "Sphere";
}
Granted, that's not remotely useful to anyone, but...as per tfa: