Deep probabilistic programming and the protein folding problem

Video recording:

Speaker: Thomas Hamelryck (Department of Computer Science, University of Copenhagen, Denmark)
Title: Deep probabilistic programming and the protein folding problem (This talk also serves as intro for the two following talks, #50 and #51)
Time: Wednesday, 2022.02.02, 10:00 a.m. (CET)
Place: fully virtual (contact Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion

Abstract: For many decades, the protein structure prediction problem has been a major open problem in science, medicine and biotechnology. It has now morphed into a paradigm problem for machine learning and computational statistics. The success of DeepMind’s AlphaFold in predicting protein structures from sequence caused the organisers of the biannual CASP contest (the “Olympic games of protein structure prediction”) to declare on November, 30th 2020: “an artificial intelligence (AI) solution to the challenge has been found”. But several practical and principled challenges remain, including the accurate modelling of the folding process and representing aleatory and epistemic uncertainty (respectively due to protein dynamics and experimental noise) with a bona fide Bayesian model. In a series of three talks, we will provide an introduction to the protein structure prediction problem, its current status and our ongoing work in this area using deep probabilistic programming, directional statistics and Stein variational inference. This work is done in collaboration with Christophe Ley at the Université du Luxembourg and Kanti Mardia at the University of Leeds.