[ExI] ‘The game has changed.’ AI triumphs at solving protein structures

Tue Dec 1 02:21:54 UTC 2020

Absolutely massive step forward. I won't be surprised if this work gets a
Nobel Prize one day. Seems to be a similar breakthrough as CRISPR.

On Mon, Nov 30, 2020 at 10:45 AM Giulio Prisco via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> Wow this seems great!
>
> https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology
>
> On 2020. Nov 30., Mon at 18:55, Dave Sill via extropy-chat <
> extropy-chat at lists.extropy.org> wrote:
>
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>> *https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures
>> <https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures>*
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>> *Artificial intelligence (AI) has solved one of biology’s grand
>> challenges: predicting how proteins curl up from a linear chain of amino
>> acids into 3D shapes that allow them to carry out life’s tasks. Today,
>> leading structural biologists and organizers of a biennial protein-folding
>> competition announced the achievement by researchers at DeepMind, a
>> U.K.-based AI company. They say the DeepMind method will have far-reaching
>> effects, among them dramatically speeding the creation of new
>> medications.“What the DeepMind team has managed to achieve is fantastic and
>> will change the future of structural biology and protein research,” says
>> Janet Thornton, director emeritus of the European Bioinformatics Institute.
>> “This is a 50-year-old problem,” adds John Moult, a structural biologist at
>> the University of Maryland, Shady Grove, and co-founder of the competition,
>> Critical Assessment of Protein Structure Prediction (CASP). “I never
>> thought I’d see this in my lifetime.”The human body uses tens of thousands
>> of different proteins, each a string of dozens to many hundreds of amino
>> acids. The order of those amino acids dictates how the myriad pushes and
>> pulls between them give rise to proteins’ complex 3D shapes, which, in
>> turn, determine how they function. Knowing those shapes helps researchers
>> devise drugs that can lodge in proteins’ pockets and crevices. And being
>> able to synthesize proteins with a desired structure could speed the
>> development of enzymes that make biofuels and degrade waste plastic.For
>> decades, researchers deciphered proteins’ 3D structures using experimental
>> techniques such as x-ray crystallography or cryo–electron microscopy
>> (cryo-EM). But such methods can take months or years and don’t always work.
>> Structures have been solved for only about 170,000 of the more than 200
>> million proteins discovered across life forms.In the 1960s, researchers
>> realized if they could work out all individual interactions within a
>> protein’s sequence, they could predict its 3D shape. With hundreds of amino
>> acids per protein and numerous ways each pair of amino acids can interact,
>> however, the number of possible structures per sequence was astronomical.
>> Computational scientists jumped on the problem, but progress was slow.In
>> 1994, Moult and colleagues launched CASP, which takes place every 2 years.
>> Entrants get amino acid sequences for about 100 proteins whose structures
>> are not known. Some groups compute a structure for each sequence, while
>> other groups determine it experimentally. The organizers then compare the
>> computational predictions with the lab results and give the predictions a
>> global distance test (GDT) score. Scores above 90 on the zero to 100 scale
>> are considered on par with experimental methods, Moult says.Even in 1994,
>> predicted structures for small, simple proteins could match experimental
>> results. But for larger, challenging proteins, computations’ GDT scores
>> were about 20, “a complete catastrophe,” says Andrei Lupas, a CASP judge
>> and evolutionary biologist at the Max Planck Institute for Developmental
>> Biology. By 2016, competing groups had reached scores of about 40 for the
>> hardest proteins, mostly by drawing insights from known structures of
>> proteins that were closely related to the CASP targets.When DeepMind first
>> competed in 2018, its algorithm, called AlphaFold, relied on this
>> comparative strategy. But AlphaFold also incorporated a computational
>> approach called deep learning, in which the software is trained on vast
>> data troves—in this case, the sequences, structures, and known proteins—and
>> learns to spot patterns. DeepMind won handily, beating the competition by
>> an average of 15% on each structure, and winning GDT scores of up to about
>> 60 for the hardest targets.But the predictions were still too coarse to be
>> useful, says John Jumper, who heads AlphaFold’s development at DeepMind.
>> “We knew how far we were from biological relevance.” To do better, Jumper
>> and his colleagues combined deep learning with a “tension algorithm” that
>> mimics the way a person might assemble a jigsaw puzzle: first connecting
>> pieces in small clumps—in this case clusters of amino acids—and then
>> searching for ways to join the clumps in a larger whole. Working on a
>> modest, 128-processor computer network, they trained the algorithm on all
>> 170,000 or so known protein structures.And it worked. Across target
>> proteins in this year’s CASP, AlphaFold achieved a median GDT score of
>> 92.4. For the most challenging proteins, AlphaFold scored a median of 87,
>> 25 points above the next best predictions. It even excelled at solving
>> structures of proteins that sit wedged in cell membranes, which are central
>> to many human diseases but notoriously difficult to solve with x-ray
>> crystallography. Venki Ramakrishnan, a structural biologist at the Medical
>> Research Council Laboratory of Molecular Biology, calls the result “a
>> stunning advance on the protein folding problem.”All of the groups in this
>> year’s competition improved, Moult says. But with AlphaFold, Lupas says,
>> “The game has changed.” The organizers even worried DeepMind may have been
>> cheating somehow. So Lupas set a special challenge: a membrane protein from
>> a species of archaea, an ancient group of microbes. For 10 years, his
>> research team tried every trick in the book to get an x-ray crystal
>> structure of the protein. “We couldn’t solve it.”But AlphaFold had no
>> trouble. It returned a detailed image of a three-part protein with two long
>> helical arms in the middle. The model enabled Lupas and his colleagues to
>> make sense of their x-ray data; within half an hour, they had fit their
>> experimental results to AlphaFold’s predicted structure. “It’s almost
>> perfect,” Lupas says. “They could not possibly have cheated on this. I
>> don’t know how they do it.”As a condition of entering CASP, DeepMind—like
>> all groups—agreed to reveal sufficient details about its method for other
>> groups to re-create it. That will be a boon for experimentalists, who will
>> be able to use accurate structure predictions to make sense of opaque x-ray
>> and cryo-EM data. It could also enable drug designers to quickly work out
>> the structure of every protein in new and dangerous pathogens like
>> SARS-CoV-2, a key step in the hunt for molecules to block them, Moult
>> says.Still, AlphaFold doesn’t do everything well yet. In the contest, it
>> faltered noticeably on one protein, an amalgam of 52 small repeating
>> segments, which distort each others’ positions as they assemble. Jumper
>> says the team now wants to train AlphaFold to solve such structures, as
>> well as those of complexes of proteins that work together to carry out key
>> functions in the cell.Even though one grand challenge has fallen, others
>> will undoubtedly emerge. “This isn’t the end of something,” Thornton says.
>> “It’s the beginning of many new things.”*
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