Department of Chemistry Seminar - Deep Learning in Protein Folding: Trajectory Reconstruction from Experimental Data and Ultra-fast Latent Space Simulators

Speaker: Professor Andrew FERGUSON

Institution: Pritzker School of Molecular Engineering, University of Chicago, USA

Hosted by: Professor Xuhui HUANG

Zoom Link: https://hkust.zoom.us/j/92142985309?pwd=QVZGblBXY1dteE9iSjBWS0g2eGJKUT09

Abstract

Data-driven modeling and deep learning present powerful tools that are opening up new paradigms and opportunities in the understanding, discovery, and design of soft and biological materials. In this talk, I will first describe an approach integrating ideas from dynamical systems theory, nonlinear manifold learning, and deep learning to reconstruct protein folding funnels and molecular structures from one-dimensional time series in experimentally measurable observables obtainable by single molecule FRET. I will then describe how we have used these ideas to train highly efficient latent space molecular simulators to generate molecular folding trajectories millions of times faster than molecular dynamics simulations.

About the speaker

Professor Andrew Ferguson is an Associate Professor and Deputy Dean for Equity, Diversity, and Inclusion at the Pritzker School of Molecular Engineering at the University of Chicago. He received an M.Eng. in Chemical Engineering from Imperial College London in 2005, and a Ph.D. in Chemical and Biological Engineering from Princeton University in 2010. From 2010 to 2012 he was a Postdoctoral Fellow of the Ragon Institute of MGH, MIT, and Harvard in the Department of Chemical Engineering at MIT. He commenced his independent career as an Assistant Professor of Materials Science and Engineering at the University of Illinois at Urbana-Champaign in August 2012 and was promoted to Associate Professor of Materials Science and Engineering and Chemical and Biomolecular Engineering in January 2018. He joined the Pritzker School of Molecular Engineering in July 2018. His research uses theory, simulation, and machine learning to understand and design self-assembling materials, macromolecular folding, and antiviral therapies. He is the recipient of a 2020 Dreyfus Foundation Award for Machine Learning in the Chemical Sciences and Engineering, 2018/19 Junior Moulton Medal of the Institution of Chemical Engineers, 2017 UIUC College of Engineering Dean's Award for Excellence in Research, 2016 AIChE CoMSEF Young Investigator Award for Modeling & Simulation, 2015 ACS OpenEye Outstanding Junior Faculty Award, 2014 NSF CAREER Award, 2014 ACS PRF Doctoral New Investigator, and was named the Institution of Chemical Engineers North America 2013 Young Chemical Engineer of the Year.