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Benchmarking the thermodynamic analysis of water molecules around a model beta sheet.

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Huggins, David J 


Water molecules play a vital role in biological and engineered systems by controlling intermolecular interactions in the aqueous phase. Inhomogeneous fluid solvation theory provides a method to quantify solvent thermodynamics from molecular dynamics or Monte Carlo simulations and provides an insight into intermolecular interactions. In this study, simulations of TIP4P-2005 and TIP5P-Ewald water molecules around a model beta sheet are used to investigate the orientational correlations and predicted thermodynamic properties of water molecules at a protein surface. This allows the method to be benchmarked and provides information about the effect of a protein on the thermodynamics of nearby water molecules. The results show that the enthalpy converges with relatively little sampling, but the entropy and thus the free energy require considerably more sampling to converge. The two water models yield a very similar pattern of hydration sites, and these hydration sites have very similar thermodynamic properties, despite notable differences in their orientational preferences. The results also predict that a protein surface affects the free energy of water molecules to a distance of approximately 4.0 Å, which is in line with previous work. In addition, all hydration sites have a favorable free energy with respect to bulk water, but only when the water-water entropy term is included. A new technique for calculating this term is presented and its use is expected to be very important in accurately calculating solvent thermodynamics for quantitative application.



Benchmarking, Models, Molecular, Protein Structure, Secondary, Proteins, Thermodynamics, Water

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J Comput Chem

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Engineering and Physical Sciences Research Council (EP/F032773/1)
Medical Research Council (G0700651)
Medical Research Council (G1001522)
Wellcome Trust (091058/Z/09/C)
Wellcome Trust (091058/Z/09/Z)
Acknowledgements go to Mike Payne for careful reading of the manuscript, Stuart Rankin for technical help and the NVIDIA CUDA Centre of Excellence at the Cambridge HPCS for use of the CUDA-accelerated GPUs. All calculations were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service ( provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and were funded by the EPSRC under grant EP/F032773/1. Thanks for financial support go to the MRC, Wellcome Trust and EPSRC.