Predicting (and discovering) proteins with multiple conformational states
Tuesday May 23rd, 4-5 pm EST | Hannah Wayment-Steele — Postdoctoral Scholar,
Brandeis University
Abstract: AlphaFold2 (AF2) has revolutionized structural biology by accurately predicting single structures of proteins; however, biological function is rooted in a protein’s ability to sample different conformational substates. This has sparked immense interest in expanding AF2’s capability to predict conformational substates. We demonstrate that clustering an input MSA by sequence similarity enables AF2 to sample alternate states of known metamorphic proteins, and and score these states with high confidence. We used our clustering method to screen for alternate states in protein families without known fold-switching, and identified a putative alternate state for the oxidoreductase DsbE. Similarly to KaiB, DsbE is predicted to switch between a thioredoxin-like fold and a novel fold. Further development of such bioinformatics methods in tandem with experiments will likely have significant impact for predicting protein energy landscapes, essential for shedding light on their biological function.
Preprint: https://www.biorxiv.org/content/10.1101/2022.10.17.512570v1
Website: https://hwaymentsteele.github.io/
Recording link: https://youtu.be/T5yknC0tr50
Hannah is a Jane Coffin Childs postdoctoral fellow working with Dorothee Kern at Brandeis University. She studied chemistry and mathematics at Pomona College, and completed an M.Phil. conducting research in theoretical chemistry with Daan Frenkel at Cambridge University as a Churchill Scholar. She completed her PhD at Stanford University working with Vijay Pande and Rhiju Das, where the science she's proudest of was creating improved models for RNA structure ensemble prediction and contributing to developing more shelf-stable RNA vaccines.
Outside of science, she spends a lot of time rowing, and in 2019 and 2021 represented the USA at the World Rowing Coastal Championships.