University of Washington
Talk Session: SESSION 10: NEW HORIZONS IN PEPTIDE SCIENCE: GREEN METHODS AND DATA SCIENCE
Date: Wednesday, June 15, 2022
Talk Time: 08:25 am - 08:55 am
Talk Title: Protein Design Using Deep Learning
Professor Baker's research is focused on the prediction and design of protein structures, protein folding mechanisms, protein-protein interactions, protein-nucleotide interactions, and protein-ligand interactions. His approach is to use experiments to understand the fundamental principles underlying these problems, to develop simple computational models based on these insights, and to test the models through structure prediction and design. The members of his research group strive to continually improve their methodology by iterating between computational and experimental studies.
- Designing molecular switches, enzymes, and motors
- Designing delivery vehicles for targeted intracellular delivery of biologics
- Designing smart protein therapeutics that carry out logic operations in the body
- Designing high-affinity binders to arbitrary small molecule and protein targets
- Designing membrane-permeable macrocyclic peptide therapeutics
- Generating novel hybrid materials through designed biomineralization
- Deep learning for protein structure refinement and protein design
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. I will also describe the systematic design of rigid membrane permeable macrocycles 6-12 amino acids in length, and smaller rigid macrocycles built from a wide diversity of backbone chemistries.