BioML Seminar 4.5 - Deep Models of Protein Evolution in Time
[VIRTUAL SEMINAR OVER ZOOM]
Join us for a new seminar from the BioML group in Machine Learning at Berkeley, sponsored by Amplify Partners. This week, we're excited to host Antoine Koehl, a postdoctoral researcher at UC Berkeley!
Abstract: Models of protein evolution are foundational to biology, underpinning essential techniques such as phylogenetic tree inference, ancestral sequence reconstruction, multiple sequence alignment, variant effect prediction, and protein design. Historically, for computational tractability, these models have relied on the simplifying – but biologically unrealistic – assumption that sites in a given protein evolve independently of each other. A crucial test of any evolutionary model is its ability to simulate realistic evolutionary trajectories, but the independent-sites assumption leads to simulations that poorly reflect the complexity of natural protein evolution. Here we introduce PEINT (Protein Evolution IN Time), a flexible and generalizable deep learning framework for modeling how the entire protein sequence evolves over time while incorporating complex interactions between sites. PEINT enables realistic simulation of protein evolution that explores new sequence space while respecting structural and functional constraints. This evolution-informed generative modeling framework offers a powerful new tool for advancing both phylogenetic inference and protein engineering.
Speaker Bio: Antoine started as a biochemist and structural biologist. He received his B.S. in Molecular, Cell and Developmental Biology from UCLA, and then went on to do his Ph.D. in Structural Biology with Brian Kobilka at Stanford University. There, he worked to understand the molecular basis of G protein-coupled receptor activation and signaling using structural and biochemical methods. This experience kicked off an interest in learning about expansion of function in protein families that share a common fold. He is currently a postdoc at UC Berkeley, having made the transition to computational biology and machine learning, advised by Yun S. Song and David Savage.