Ligand-receptor binding and unbinding are fundamental molecular processes, and are particularly essential to drug efficacy, whereas water fluctuations impact the corresponding thermodynamics and kinetics. We develop a variational implicit-solvent model (VISM), a geometrical flow model, to calculate the potential of mean force (PMF) as well as the solute-solvent interfacial structures of dry and wet states for a model ligand-pocket system. We also combine our VISM with the string method for transition paths to obtain the dry-wet transition rates, and conduct two-state Brownian dynamics simulations of the ligand stochastic motion, providing the mean first-passage times for the ligand-pocket binding and unbinding. We find that the dewetting transition around the pocket is slowed down as the ligand approaches the pocket but is peaked suddenly once the ligand enters the pocket. In contrast to binding, the ligand unbinding involves a much larger timescale due to a high energy barrier at the pocket entrance. The dry-wet fluctuation slows down the binding but accelerates the unbinding process. Without any explicit description of individual water molecules, our predictions are in a very good, qualitative and semi-quantitative, agreement with existing explicit-water molecular dynamics simulations, providing a promising step in further efficient studies of the ligand-receptor binding/unbinding kinetics. This is joint work with Shenggao Zhou, R. Gregor Weiss, Li-Tien Cheng, Joachim Dzubiella, and J. Andrew McCammon. 

Bo Li received his Ph.D. in mathematics and MS in mechanics from University of Minnesota in 1996. He did a postdoc at UCLA and was an assistant professor at the University of Maryland. Since 2004, he has been an associate and then full professor of mathematics at UC San Diego. His research interests include scientific computing and numerical analysis, and applied aspects of partial differential equations and stochastic processes, with application to materials science, continuum physics, and more recently biological physics and computational biology.