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

Wed Dec 2 00:10:24 UTC 2020

I agree, this is extremely exciting - cognisant of my excitement, is it
fair to say that protein folding has been solved?

Kind regards,

Adam A. Ford
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On Wed, 2 Dec 2020 at 04:10, Divya Cohen via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> 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:
>>
>>>
>>> *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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