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Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

preprint
submitted on 01.07.2020 and posted on 02.07.2020 by Gregory Ross, Ellery Russell, Yuqing Deng, Chao Lu, Edward Harder, Robert Abel, Lingle Wang
The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of proteinligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water.

History

Email Address of Submitting Author

gregory.ross@schrodinger.com

Institution

Schrodinger, Inc.

Country

USA

ORCID For Submitting Author

0000-0003-2452-7643

Declaration of Conflict of Interest

None

Version Notes

Version 1: initial submission.

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in Journal of Chemical Theory and Computation

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