Accessing the dynamical foundations of biology
Our research seeks a predictive understanding of biomolecular dynamics and mechanisms that underlie biological function and shape evolution.
We are bringing together data science, deep learning, and NMR spectroscopy.
Deep learning: a 10,000-ft view of all known data and patterns.
NMR: an intimate view of biomolecules' motions, interactions, and timing.
We are based at UW-Madison Biochemistry
and affiliated with Biostatistics and Medical Informatics.
Data junkie in chief
hannah.waymentsteele (at) wisc.edu
Would bring to a Deserted Island: A rowboat
Wayment-Steele HK#, Otten R#, Pitsawong W#, Ojoawo A#, Glaser A, Calderone LA & Kern D. The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale. PNAS 2024
🔗Zhang Z#, Wayment-Steele HK#, Brixi G, Wang H, Kern D & Ovchinnikov S. Protein language models learn evolutionary statistics of interacting sequence motifs. PNAS 2024
🔗Wayment-Steele HK#, Ojoawo A#, Otten R, Apitz JM, Pitsawong W, Hoemberger M, Ovchinnikov S, Colwell L & Kern D. Predicting multiple conformations via sequence clustering and AlphaFold2. Nature 2023
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We welcome applicants with backgrounds in computation, experiment, and any mix of the two!
Interested PhD applicants are encouraged to apply to the
Biophysics,
the Integrated Program in
Biochemistry,
and/or the
Biomedical Data Science
PhD programs.
Interested in post-doc or post-bac: please email
Hannah
describing your interests and goals, along with a CV.