The quality of molecular dynamics (MD) simulations relies heavily on the accuracy of the underlying force field. In recent years, considerable effort has been put into developing more accurate dihedral angle potentials for MD force fields, but relatively little work has focused on the nonbonded parameters, many of which are two decades old. In this work, we assess the accuracy of protein-protein van der Waals interactions in the AMBER ff9x/ff12 force field. Across a test set of 44 neat organic liquids containing the moieties present in proteins, we find root-mean-square (RMS) errors of 1.26 kcal/mol in enthalpy of vaporization and 0.36 g/cm(3) in liquid densities. We then optimize the van der Waals radii and well depths for all of the relevant atom types using these observables, which lowers the RMS errors in enthalpy of vaporization and liquid density of our validation set to 0.59 kcal/mol (53% reduction) and 0.019 g/cm(3) (46% reduction), respectively. Limitations in our parameter optimization were evident for certain atom types, however, and we discuss the implications of these observations for future force field development.
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Artificial intelligence (AI) is increasingly used in many medical specialties. However, nephrology has lagged in adopting and incorporating machine learning techniques. Nephrology is well positioned to capitalize on the benefits of AI.
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Laboratoire Interdisciplinaire de Physique (LIPhy), Université Grenoble Alpes, CNRS, Grenoble, France.
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Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN USA.
Severe convective storms and tornadoes rank among nature's most hazardous phenomena, inflicting significant property damage and casualties. Near-surface weather conditions are closely governed by large-scale synoptic patterns. It is crucial to delve into the involved multiscale associations to understand tornado potential in response to climate change.
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Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
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Institut Camille Jordan, UMR-CNRS 5208, École Centrale de Lyon, 36 Avenue Guy de Collongue, 69134, Écully, France.
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