AI leads to protein breakthrough

Structure of folded human proteins mapped

UK-based artificial intelligence company DeepMind recently announced that it is publishing the structures of more than 200 million proteins, with nearly all catalogued on the globally recognised repository of protein research, UniProt. The company revealed that it had mapped 98.5 per cent of the proteins in the human body.

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. DeepMind has worked with the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) to create a searchable store of all this information that can be easily and freely accessed by researchers around the world. Ewan Birney at EMBL-EBI calls the AlphaFold Protein Structure Database “a gift to humanity”.

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.

Work still to be done

Keith Willison at Imperial College London says that AlphaFold has unarguably “changed the world” of biological research, but that there are still problems to be solved in protein folding.

“As soon as AlphaFold came out it was wonderful. You just take your favourite proteins and look them up now rather than having to make crystals,” he says. “I did the crystallographic structure of a protein complex, it took me about eight years. People are joking that crystallographers are going to be unemployed.”

But Willison points out that AlphaFold isn’t able to take any arbitrary string of amino acids and model exactly how they fold. Instead, it is only able to use parts of proteins and their structures that have been experimentally determined to predict how a new protein will fold.

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.

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 wanWillt to will 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.”

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SOURCE:  NewScientist, Technology 28July 2022

DeepMind’s protein-folding AI cracks biology’s biggest problem

by Matthew Sparkes

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