An AI solution to the protein folding problem: what is it, how did it happen, and some implications

About this Class

Dear colleagues, We'll be hosting a special guest lecture by Prof. John Moult from UMD. Abstract: Computing the three-dimensional structure of a protein molecule from its amino acid sequence is a long-standing grand challenge problem. Results from the recent Critical Assessment of Structure Prediction (CASP14) experiment show that new deep-learning methods have now provided a dramatic solution, with many computed structures comparable, likely sometimes better, representations of in vivo protein structures to those obtained with state-of-the-art experimental techniques of crystallography and cryo-electron microscopy. These models have already demonstrated an ability to solve problematic crystal structures, and the results suggest the methods will be successfully applied to other areas of structural biology and more generally. This is the first solution of a serious scientific problem by AI, and it will not be the last. In this talk I’ll describe how the protein modeling field arrived at this point, what sort of methods were used, characteristics of the computed structures, and some potential further applications. Bio: John Moult is a Fellow at the Institute for Bioscience and Biotechnology Research and Professor in the Department of Cell Biology and Molecular Genetics at the University of Maryland. He is co-founder and Chair of CASP (Critical Assessment of Protein structure Prediction), an organization that conducts large-scale experiments in protein structure modeling, and joint founder of CAGI, a sister organization for advancing genome interpretation. He is an ex-crystallographer turned computational biologist. His research interests include the relationship between genetic variation and human disease, disease mechanisms, protein structure, and different ways of doing science. (BSc Physics, University of London 1965, D.Phil Molecular Biophysics, University of Oxford 1970) Join Zoom Meeting https://umd.zoom.us/j/97941931766 Meeting ID: 979 4193 1766 One tap mobile +13017158592,,97941931766# US (Washington DC) +13126266799,,97941931766# US (Chicago)