Chen Y, Lu T, Zhao C, Wayment-Steele HK, Huang P. SLAE: Strictly Local All-atom Environment for Protein Representation. BioRXiv 2025 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 Wayment-Steele HK#, Kladwang, W#, Watkins, AM#, Kim, DS#, Tunguz, B#, … Das, R. Deep learning models for predicting RNA degradation via dual crowdsourcing. Nature Machine Intelligence 2022 Wayment-Steele HK, Kladwang, W, Strom, A. I., Becka, A., Lee, J., Treuille, A., Eterna Participants, Das, R. RNA secondary structure packages evaluated and improved by high-throughput experiments. Nature Methods 2022 Leppek, K.#, Byeon, GW#, Kladwang, W#, Wayment-Steele HK#, Kerr, CH#, … Barna, M, Das, R. Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. Nature Communications 2022 Andreasson, JO, Gotrik, MR, Wu, MJ, Wayment-Steele HK, Kladwang, W, Portela, F, Wellington-Oguri, R., Eterna Participants, Das, R., Greenleaf, W. J. Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular sensors. PNAS 2022 Wayment-Steele HK, Kim DS, Choe CA, Nicol JJ, Wellington-Oguri R, Sperberg RAP, Huang P, Eterna Participants, Das R. Theoretical basis for stabilizing messenger RNA through secondary structure design. Nucleic Acids Research 2021 Kostrz D, Wayment-Steele HK, Wang JL, Follenfant M, Pande VS, Strick TR, Gosse C. A modular DNA scaffold to study protein–protein interactions at single-molecule resolution. Nature Nanotechnology 2019 Wayment-Steele HK, Pande, VS Variational encoding of protein dynamics benefits from maximizing latent autocorrelation. The Journal of Chemical Physics 2018 Hernandez, CX#, Wayment-Steele, HK#, Sultan, MM#, Husic, BE, Pande, VS. Variational Encoding of Complex Dynamics. Physical Review E 2018 Sultan, MM, Wayment-Steele HK, Pande, VS. Transferable neural networks for enhanced sampling of protein dynamics. Journal of Chemical Theory and Computation 2018 Husic, BE, McKiernan, KA, Wayment-Steele HK, Sultan, MM, Pande, VS. A minimum variance clustering approach produces robust and interpretable coarse-grained models. Journal of Chemical Theory and Computation 2018Publications