Author

H. K. Wayment-Steele

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 2 Biochemistry· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Approach:statistical model· 3 Research Focus:sequence-to-function modeling· 2 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1 Assay:long-read sequencing· 1 Assay:multiome· 1 Research Focus:protein design· 1

Frequent Co-Authors

R. Das· 2 shared A. I. Strom· 1 shared A. M. Watkins· 1 shared A. Treuille· 1 shared C. A. Choe· 1 shared D. Kern· 1 shared D. S. Kim· 1 shared Eterna Massive Open Laboratory· 1 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersBiophysics

R. Das

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Author profile →

3 papers

Recent·Most cited

BiochemistrybioRxivPreprintOct 17, 2022

Prediction of multiple conformational states by combining sequence clustering with AlphaFold2

H. K. Wayment-Steele, S. Ovchinnikov, L. Colwell, D. Kern

The paper uses sequence clustering with AlphaFold2 to predict conformational states of proteins and identifies mutations that switch between states.

DOI 10.1101/2022.10.17.512570

protein structuredeep learning

𝕏 PostBluesky

BiophysicsbioRxivAug 22, 2020

Theoretical basis for stabilizing messenger RNA through secondary structure design

H. K. Wayment-Steele, D. S. Kim, C. A. Choe…·R. Das

This paper develops a biophysical model to predict RNA stability and demonstrates that superfolder mRNAs can increase in vitro half-life by at least two-fold through AUP optimization.

DOI 10.1093/nar/gkab764

multiomedeep learning

𝕏 PostBluesky

BiophysicsbioRxivPreprintMay 29, 2020

RNA secondary structure packages evaluated and improved by high-throughput experiments

H. K. Wayment-Steele, W. Kladwang, A. I. Strom…·R. Das

The paper evaluates RNA structure prediction packages using synthetic data and introduces an improved model that performs better on diverse datasets.

DOI 10.1101/2020.05.29.124511

long-read sequencingproteomics

𝕏 PostBluesky