Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers

Nassar, Roy; Brini, Emiliano; Parui, Sridip; Liu, Cong; Dignon, Gregory L.; Dill, Ken A.

doi:10.1021/acs.jctc.1c00916

Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers

Journal Article · 08 February 2022 · Journal of Chemical Theory and Computation

DOI:https://doi.org/10.1021/acs.jctc.1c00916· OSTI ID:1981882

^[1]; ^[2]; ^[2]; ^[1]; Dignon, Gregory L. ^[2]; ^[3]

Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States; Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States; Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States; Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States

Not provided.

Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1981882

Journal Information:: Journal of Chemical Theory and Computation, Vol. 18, Issue 3; ISSN 1549-9618

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

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