Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields

TL;DR

This review discusses structurally oriented experimental datasets from NMR and crystallography used to benchmark and assess the accuracy of protein force fields in molecular dynamics simulations.

Contribution

It provides a comprehensive overview of experimental datasets and their connection to force field validation, highlighting statistical considerations.

Findings

NMR and crystallography data inform force field accuracy

Connecting experimental observables to simulations enhances benchmarking

Statistical issues are important in comparing data and simulations

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.