143 citations · 363 across the 7 of their papers we have counts for
7 papers
Expanding Protein Structure Prediction into Conformational State Space
Devlina Chakravarty, Justin J. Miller, Da Teng +8
Recent AI advances have enabled protein structure prediction at near-experimental accuracy, largely solving the problem of identifying a dominant conformation from sequence. Many p…
Folding@home: achievements from over twenty years of citizen science herald the exascale era
Vincent A. Voelz, Vijay S. Pande, Gregory R. Bowman
Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home dis…
Remembering the work of Phillip L. Geissler: A coda to his scientific trajectory
Gregory R. Bowman, Stephen J. Cox, Christoph Dellago +19
Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He dev…
Choice of adaptive sampling strategy impacts state discovery, transition probabilities, and the apparent mechanism of conformational changes
Maxwell I. Zimmerman, Justin R. Porter, Xianqiang Sun +2
Interest in equilibrium-based sampling methods has grown with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain th…
Hierarchical Nystrom Methods for Constructing Markov State Models for Conformational Dynamics
Yuan Yao, Raymond Z. Cui, Gregory R. Bowman +3
Markov state models (MSMs) have become a popular approach for investigating the conformational dynamics of proteins and other biomolecules. MSMs are typically built from numerous m…
Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty
Gregory R. Bowman
Markov state models (MSMs)---or discrete-time master equation models---are a powerful way of modeling the structure and function of molecular systems like proteins. Unfortunately,…