2022 Lemieux Lecture: Protein Design Using Deep Learning
Apr 13, 2022 — All day
Abstract
Proteins mediate the critical processes of life and beautifully solve the challenges faced during the evolution of modern organisms. Our goal is to design a new generation of proteins that address current-day problems not faced during evolution. In contrast to traditional protein engineering efforts, which have focused on modifying naturally occurring proteins, we design new proteins from scratch to optimally solve the problem at hand. We now use two approaches. First, guided by Anfinsen’s principle that proteins fold to their global free energy minimum, we use the physically based Rosetta method to compute sequences for which the desired target structure has the lowest energy. Second, we use deep learning methods to design sequences predicted to fold to the desired structures. In both cases, following the computation of amino acid sequences predicted to fold into proteins with new structures and functions, we produce synthetic genes encoding these sequences, and characterize them experimentally. In this talk, I will describe recent advances in protein design using both approaches.

Prof. David Baker
University of Washington
David Baker is the director of the Institute for Protein Design, a Howard Hughes Medical Institute Investigator, a professor of biochemistry, and an adjunct professor of genome sciences, bioengineering, chemical engineering, computer science, and physics at the University of Washington. Dr. Baker has published over 550 research papers, been granted over 100 patents, and co-founded 17 companies. Sixty-eight of his mentees have gone on to independent faculty positions. Dr. Baker is a recipient of the Breakthrough Prize in Life Sciences and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences.
Everyone is welcome.
Contact info
Dr. Eva Hemmer
Associate Professor, Department of Chemistry and Biomolecular Sciences
Phone: +1-613-562-5800 ext. 1987
Website: www.hemmerlab.com