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In a major scientific breakthrough, A.I. predicts the exact shape of proteins

https://fortune.com/2020/11/30/deepmind-protein-folding-breakthrough/

Across more than 100 proteins, DeepMind’s A.I. software, which it called AlphaFold 2, was able to predict the structure to within about an atom’s width of accuracy in two-thirds of cases and was highly accurate in most of the remaining one-third of cases, according to John Moult, a molecular biologist at the University of Maryland who is director of the competition, called the Critical Assessment of Structure Prediction, or CASP. It was far better than any other method in the competition, he said.

Janet Thornton, an expert in protein structure and former director of the European Molecular Biology Laboratory’s European Bioinformatics Institute, said that DeepMind’s breakthrough opened up the way to mapping the entire “human proteome”—the set of all proteins found within the human body. Currently, only about a quarter of human proteins have been used as targets for medicines, she said. Now, many more proteins could be targeted, creating a huge opportunity to invent new medicines.

Thornton also said that DeepMind’s A.I. system would have profound implications for scientists who create synthetic proteins and that these could have big impacts too: everything from creating new genetically modified crop strains that will be far more nutritious to new enzymes that could help clean up the environment by digesting plastics.

Until now, the primary way to obtain a high-resolution model of a protein’s structure was through a method called X-ray crystallography. In this technique, a solution of proteins is turned into a crystal, itself a difficult and time-consuming process, and then this crystal is bombarded with X-rays, often from a large circular particle accelerator called a synchrotron. The diffraction pattern of the X-rays allows researchers to build up a picture of the internal structure of the protein. It takes about a year and costs about $120,000 to obtain the structure of a single protein through X-ray crystallography, according to an estimate from the University of Toronto.

As part of CASP’s efforts to verify the capabilities of DeepMind’s system, Lupas used the predictions from AlphaFold 2 to see if it could solve the final portion of a protein’s structure that he had been unable to complete using X-ray crystallography for more than a decade. With the predictions generated by AlphaFold 2, Lupas said he was able to determine the shape of the final protein segment in just half an hour.