Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v0.1]

  • Chapin E Cavender*
  • , David A Case
  • , Julian C-H Chen
  • , Lillian T Chong
  • , Daniel A Keedy
  • , Samuli Ollila
  • , Kresten Lindorff-Larsen
  • , David L Mobley
  • , Chris Oostenbrink
  • , Paul Robustelli
  • , Vincent A Voelz
  • , Michael E Wall
  • , David C Wych
  • , Michael K Gilson
  • *Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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 statistical issues that arise when comparing simulations with experiment. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations.

Original languageEnglish
Article number3871
Number of pages47
JournalLiving Journal of Computational Molecular Science
Volume6
Issue number1
DOIs
Publication statusPublished - 25 Mar 2025
MoE publication typeA1 Journal article-refereed

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