Prediction of multiple dry–wet transition pathways with a mesoscale variational approach

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Abstract
Water fluctuates in a hydrophobic confinement, forming multiple dry and wet hydration states through evaporation and condensation. Transitions between such states are critical to both thermodynamics and kinetics of solute molecular processes, such as protein folding and protein–ligand binding and unbinding. To efficiently predict such dry–wet transition paths, we develop a hybrid approach that combines a variational implicit solvation model, a generalized string method for minimum free-energy paths, and the level-set numerical implementation. This approach is applied to three molecular systems: two hydrophobic plates, a carbon nanotube, and a synthetic host molecule Cucurbit[7]uril. Without an explicit description of individual water molecules, our mesoscale approach effectively captures multiple dry and wet hydration states, multiple dry–wet transition paths, such as those geometrically symmetric and asymmetric paths, and transition states, providing activation energy barriers between different states. Further analysis shows that energy barriers depend on mesoscopic lengths, such as the separation distance between the two plates and the cross section diameter of the nanotube, and that the electrostatic interactions strongly influence the dry–wet transitions. With the inclusion of solute atomic motion, general collective variables as reaction coordinates, and the finite-temperature string method, together with an improved treatment of continuum electrostatics, our approach can be further developed to sample an ensemble of transition paths, providing more accurate predictions of the transition kinetics.
Funding Information
  • National Natural Science Foundation of China (21773165, 11871365)
  • Natural Science Foundation of Jiangsu Province, China (BK20200098)
  • National Key R&D Program of China (2018YFB0204404)
  • Natural Science Foundation of Jiangsu Higher Education Institutions, China (19KJB110020)
  • National Science Foundation (DMS-1913144)
  • National Institutes of Health (R01GM132106)

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