Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0]

Chapin E. Cavender; David A. Case; Julian C.-H. Chen; Lillian T. Chong; Daniel A. Keedy; Kresten Lindorff-Larsen; David L. Mobley; O. H. Samuli Ollila; Chris Oostenbrink; Paul J. Robustelli; Vincent A. Voelz; Michael E. Wall; David C. Wych; Michael K. Gilson

doi:10.33011/livecoms.6.1.3871

Authors

Chapin E. Cavender Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA https://orcid.org/0000-0002-5899-7953
David A. Case Department of Chemistry & Chemical Biology, Rutgers University, Piscataway, NJ, USA https://orcid.org/0000-0003-2314-2346
Julian C.-H. Chen Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA; Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH, USA https://orcid.org/0000-0003-0341-165X
Lillian T. Chong Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA https://orcid.org/0000-0002-0590-483X
Daniel A. Keedy Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY, USA; Department of Chemistry and Biochemistry, City College of New York, New York, NY, USA; PhD Programs in Biochemistry, Biology, and Chemistry, CUNY Graduate Center, New York, NY, USA https://orcid.org/0000-0002-9184-7586
Kresten Lindorff-Larsen Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark https://orcid.org/0000-0002-4750-6039
David L. Mobley Department of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, USA https://orcid.org/0000-0002-1083-5533
O. H. Samuli Ollila Institute of Biotechnology, University of Helsinki, Helsinki, Finland; VTT Technical Research Centre of Finland, Espoo, Finland https://orcid.org/0000-0002-8728-1006
Chris Oostenbrink Institute for Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Vienna, Austria https://orcid.org/0000-0002-4232-2556
Paul J. Robustelli Department of Chemistry, Dartmouth College, Hanover, NH, USA https://orcid.org/0000-0002-9282-8993
Vincent A. Voelz Department of Chemistry, Temple University, Philadelphia, PA, USA https://orcid.org/0000-0002-1054-2124
Michael E. Wall Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA; The Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA https://orcid.org/0000-0003-1000-688X
David C. Wych Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA; The Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA https://orcid.org/0000-0001-9209-4371
Michael K. Gilson Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA https://orcid.org/0000-0002-3375-1738

DOI:

https://doi.org/10.33011/livecoms.6.1.3871

Keywords:

Molecular simulation, Force field, X-ray crystallography, NMR spectroscopy

Abstract

This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss what the observables are, what they tell us about structure and dynamics, what makes them useful for assessing force field accuracy, and how they can be connected to molecular dynamics simulations carried out using the force field one wishes to benchmark. We also touch on best practices for setup and analysis of benchmark simulations. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields or machine learning models that generate protein ensembles.

Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0]

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