computational modeling ai-structure
University of Wisconsin-Madison
Info
Assistant Professor of Biochemistry at the University of Wisconsin-Madison. Seeks a quantitative and predictive understanding of biomolecular conformations and dynamics, and how evolution shapes dynamics and function. Uses a combination of deep learning, statistical mechanics, NMR, and computational approaches. PhD in Chemistry at Stanford with Rhiju Das (machine learning for RNA structure prediction; contributed to improved mRNA vaccine designs). Postdoctoral work at Brandeis. Former visiting researcher at Google Brain. Churchill Scholar, NSF GRFP Fellow, Jane Coffin Childs Postdoctoral Fellow.
DiffUSE relevance
Wayment-Steele’s central question - “where’s the big data for biomolecular dynamics?” (the tagline on her website) - is exactly the question DiffUSE is trying to answer from the experimental side. Her work on predicting multiple conformational states from AlphaFold2 ensembles and on using deep learning to model protein dynamics is the computational counterpart to diffuse scattering as an experimental probe of those same dynamics.
Key Relationships
- University of Wisconsin-Madison Department of Biochemistry — current position
- Rhiju Das (Stanford) — PhD advisor; RNA structure prediction and vaccine design
- Google Brain — visiting researcher
- AlphaFold2 multi-state prediction — key contributor to using AF2 for conformational ensemble generation
- Protein dynamics / ML community
Sources
- Lab website: https://waymentsteelelab.org/
- Personal page: https://hwaymentsteele.github.io/
- Wisconsin profile: https://biochem.wisc.edu/people/wayment-steele/