| Autor: |
Ge Y; Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States., Wych DC; Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States.; Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Samways ML; School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom., Wall ME; Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Essex JW; School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom., Mobley DL; Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States.; Department of Chemistry, University of California, Irvine, California 92697, United States. |
| Abstrakt: |
Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation time scales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand , using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed the rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed. |
