Highly accurate protein structure prediction with AlphaFold

Predicting a protein’s structure from its primary sequence has been a grand challenge in biology for the past 50 years, holding the promise to bridge the gap between the pace of genomics discovery and resulting structural characterization. In this talk, we will describe work at DeepMind to develop AlphaFold, a new deep learning-based system for structure prediction that achieves high accuracy across a wide range of targets.  We demonstrated our system in the 14th biennial Critical Assessment of Protein Structure Prediction (CASP14) across a wide range of difficult targets, where the assessors judged our predictions to be at an accuracy “competitive with experiment” for approximately 2/3rds of proteins. The talk will focus on the underlying machine learning ideas, while also touching on the implications for biological research.

Bio: Michael Figurnov is a Staff Research Scientist at DeepMind. He has been working with the AlphaFold team for the past four years. Before joining DeepMind, he did his Ph.D. in Computer Science at the Bayesian Methods Research Group under the supervision of Dmitry Vetrov. His research interests include deep learning, Bayesian methods, and machine learning for biology.

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