For biologists who  study the structure of proteins, the recent history of their field is divided into two epochs: before CASP14, the 14th biennial ro

Without Code for DeepMind’s Protein AI, This Lab Wrote Its Own

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2021-08-12 15:30:10

For biologists who study the structure of proteins, the recent history of their field is divided into two epochs: before CASP14, the 14th biennial round of the Critical Assessment of Protein Structure conference, and after. In the decades before, scientists had spent years slowly chipping away at the problem of how to predict the structure of a protein from the sequence of amino acids that it comprises. After CASP14, which took place in December 2020, the problem had effectively been solved, by researchers at the Google subsidiary DeepMind.

A research company focused on a branch of artificial intelligence known as “deep learning,” DeepMind had previously made headlines by building an AI system that beat the Go world champion. But their success at protein structure prediction, which they achieved using a neural network they call AlphaFold2, represented the first time they had built a model that could solve a problem of real scientific relevance. Helping scientists figure out what proteins look like can facilitate research into the inner workings of cells and, by revealing ways to inhibit the action of particular proteins, potentially aid in the process of drug discovery. On July 15, the journal Nature published an unedited manuscript detailing the workings of DeepMind’s model, and DeepMind shared their code publicly.

But in the seven months since CASP, another team had taken up that mantle. In June, a full month before the publication of DeepMind’s manuscript, a team led by David Baker, director of the Institute for Protein Design at the University of Washington, released their own model for protein structure prediction. For a month, this model, called RoseTTAFold, was the most successful protein prediction algorithm that other scientists could actually use. Though it did not reach the same peaks of performance as AlphaFold2, the team ensured the model would be accessible to even the least computationally inclined scientist by building a tool that allowed researchers to submit their amino acid sequences and get back predictions, without getting their hands dirty with computer code. A month later, on the very same day that Nature released the DeepMind early manuscript, the journal Science published the Baker Lab’s paper describing RoseTTAFold.

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