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Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge

Cited 22 time in Web of Science Cited 24 time in Scopus
Authors

Koenig, Gerhard; Pickard, Frank C.; Huang, Jing; Simmonett, Andrew C.; Tofoleanu, Florentina; Lee, Ju Yong; Dral, Pavlo O.; Prasad, Samarjeet; Jones, Michael; Shao, Yihan; Thiel, Walter; Brooks, Bernard R.

Issue Date
2016-11
Publisher
Kluwer Academic Publishers
Citation
Journal of Computer-Aided Molecular Design, Vol.30 No.11, pp.989-1006
Abstract
One of the central aspects of biomolecular recognition is the hydrophobic effect, which is experimentally evaluated by measuring the distribution coefficients of compounds between polar and apolar phases. We use our predictions of the distribution coefficients between water and cyclohexane from the SAMPL5 challenge to estimate the hydrophobicity of different explicit solvent simulation techniques. Based on molecular dynamics trajectories with the CHARMM General Force Field, we compare pure molecular mechanics (MM) with quantum-mechanical (QM) calculations based on QM/MM schemes that treat the solvent at the MM level. We perform QM/MM with both density functional theory (BLYP) and semi-empirical methods (OM1, OM2, OM3, PM3). The calculations also serve to test the sensitivity of partition coefficients to solute polarizability as well as the interplay of the quantum-mechanical region with the fixed-charge molecular mechanics environment. Our results indicate that QM/MM with both BLYP and OM2 outperforms pure MM. However, this observation is limited to a subset of cases where convergence of the free energy can be achieved.
ISSN
0920-654X
URI
https://hdl.handle.net/10371/201530
DOI
https://doi.org/10.1007/s10822-016-9936-x
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

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