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Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models

Journal article
Authors Samuel Genheden
Published in Journal of Computer-Aided Molecular Design
Volume 31
Issue 10
Pages 867-876
ISSN 0920-654X
Publication year 2017
Published at Department of Chemistry and Molecular Biology
Pages 867-876
Language en
Links https://doi.org/10.1007/s10822-017-...
Keywords Solvation free energies, Partition coefficients, Coarse graining, MARTINI model, Hybrid modeling, force-field, biomolecular simulations, molecular-dynamics, membrane, generation, prediction, proteins, solutes, solvent, water, Biochemistry & Molecular Biology, Biophysics, Computer Science
Subject categories Computer and Information Science, Biophysics, Biochemistry and Molecular Biology

Abstract

We present the estimation of solvation free energies of small solutes in water, n-octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

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