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• W4309367890 abstract "Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available." @default.
• W4309367890 created "2022-11-26" @default.
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• W4309367890 date "2022-11-17" @default.
• W4309367890 modified "2023-10-16" @default.
• W4309367890 title "Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data" @default.
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• W4309367890 doi "https://doi.org/10.1021/acs.jctc.2c00657" @default.
• W4309367890 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36395419" @default.
• W4309367890 hasPublicationYear "2022" @default.
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