Author

M. D. Ward

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· 4 Biochemistry· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Research Focus:drug response / drug discovery· 3 Research Focus:host-pathogen & immune response· 2 Research Focus:protein folding & dynamics· 2 Assay:protein structure· 2 Approach:statistical model· 2 Organism:virus· 2 Approach:biophysical modeling· 1

Frequent Co-Authors

G. R. Bowman· 5 shared M. I. Zimmerman· 4 shared N. Vithani· 4 shared S. Singh· 3 shared A. Meller· 2 shared J. H. Borowsky· 2 shared A. M. Harbison· 1 shared A. S. Holehouse· 1 shared

Activity by Year

2020-2022 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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5 shared papersBiophysics

G. R. Bowman

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Author profile →

5 papers

Recent·Most cited

Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization

R. R. Eguchi, C. A. Choe, U. Parekh, I. S. Khalek, M. D. Ward, N. Vithani, G. R. Bowman, J. G. Jardine, P. Huang

This paper introduces Sculptor, a deep generative algorithm for designing epitope-specific binding proteins by optimizing loop conformations and backbone structures.

bioRxiv · Dec 22, 2022 · PreprintBiochemistry

SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential

N. Vithani, M. D. Ward, M. I. Zimmerman, B. Novak, J. H. Borowsky, S. Singh, G. R. Bowman

This paper investigates how Nsp10 activates Nsp16 and identifies a cryptic pocket in SARS-CoV-2 Nsp16 that could be targeted for pan-coronavirus antivirals.

bioRxiv · Dec 10, 2020Biophysics

DiffNets: Self-supervised deep learning to identify the mechanistic basis for biochemical differences between protein variants

M. D. Ward, M. I. Zimmerman, A. Meller, M. Chung, S. J. Swamidass, G. R. Bowman

DiffNets identifies structural features predicting biochemical differences between protein variants using self-supervised learning.

bioRxiv · Jul 1, 2020 · PreprintBiophysics

SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome

M. I. Zimmerman, J. R. Porter, M. D. Ward, S. Singh, N. Vithani, A. Meller, U. L. Mallimadugula, C. E. Kuhn, J. H. Borowsky, R. P. Wiewiora, M. F. D. Hurley, A. M. Harbison, C. A. Fogarty, J. E. Coffland, E. Fadda, , G. R. Bowman

The paper uses exascale simulations to study SARS-CoV-2 protein dynamics, revealing cryptic epitopes and conformational changes that inform antiviral target design.

bioRxiv · Jun 27, 2020 · PreprintBiophysics

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA

J. Cubuk, J. J. Alston, J. J. Incicco, S. Singh, M. D. Stuchell-Brereton, M. D. Ward, M. I. Zimmerman, N. Vithani, D. Griffith, J. A. Wagoner, G. R. Bowman, K. B. Hall, A. Soranno, A. S. Holehouse

This study investigates the SARS-CoV-2 nucleocapsid protein's dynamics, disorder, and RNA phase separation using single-molecule spectroscopy and simulations, revealing its multivalent RNA binding properties and phase separation behavior.

bioRxiv · Jun 17, 2020Biophysics