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Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields

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arxiv 2303.11056 v2 pith:JFNQZSMM submitted 2023-03-02 q-bio.BM physics.bio-phphysics.comp-ph

classification q-bio.BMphysics.bio-phphysics.comp-ph
keywords forceproteinbenchmarkfieldssimulationswhatarticledatasets
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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.

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Cited by 1 Pith paper

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  1. Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy

    physics.chem-ph 2026-07 conditional novelty 7.0 of 10

    TWIN, a MACE-based implicit-solvent MLP trained solely on ab initio and experimental data, transfers across drugs, peptides and proteins with near-DFT accuracy at ~100 imes lower cost.

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