Finance Report

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“This problem has been around for 50 years; lots of people have broken their head on it,” says John Moult, a structural biologist at the University of Maryland, Shady Grove. But a practical solution is in their grasp. Several months ago, in a result hailed as a turning point, computational biologists showed that artificial intelligence (AI) could accurately predict protein shapes. That group learn here describes its approach online in today. Meanwhile, David Baker and Minkyung Baek at the University of Washington, Seattle, and their colleagues present their AI-based structure prediction approach online in . Their method works on not just simple proteins, but also complexes of proteins. Baker’s and Baek’s method and computer code have been available for weeks, and the team has already used it to model more than 4500 protein sequences submitted by other researchers. Savvas Savvides, a structural biologist at Ghent University, had tried six times to model a problematic protein. He says Baker’s and Baek’s program, called RoseTTAFold, “paved the way to a structure solution.” In fall of 2020, DeepMind, a U.K.-based AI company owned by Google, wowed the field with its structure predictions in a biennial competition . Called Critical Assessment of Protein Structure Prediction (CASP), the competition uses structures newly determined using laborious lab techniques such as x-ray crystallography as benchmarks. DeepMind’s program, AlphaFold2, did “really extraordinary things [predicting] protein structures with atomic accuracy,” says Moult, who organizes CASP. But for many structural biologists, AlphaFold2 was a tease: “Incredibly exciting but also very frustrating,” says David Agard, a structural biophysicist at the University of California, San Francisco. In mid-June, 3 days after the Baker lab posted its RoseTTAFold preprint, Demis Hassabis, DeepMind’s CEO, tweeted that AlphaFold2’s details were under review at a publication and the company would provide “broad free access to AlphaFold for the scientific community.” has now rushed to publish that paper to coincide with the paper. “It is appropriate that it is not coming out after ours, as our work is really based on their advances,” Baker says. DeepMind’s 30-minute presentation at CASP had been enough to inspire Baek to develop her own approach. Like AlphaFold2, it uses AI’s ability to discern patterns in vast databases of examples, generating ever more informed and accurate iterations as it learns.

https://www.sciencemag.org/news/2021/07/researchers-unveil-phenomenal-new-ai-predicting-protein-structures

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