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SUMMARY:Bo Li (University of California\, San Diego)
DTSTART:20200603T150000Z
DTEND:20200603T160000Z
DTSTAMP:20260423T022835Z
UID:GSMMA/6
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/GSMMA/6/">Va
 riational implicit-solvent predictions of the ligand-receptor (un)binding 
 kinetics</a>\nby Bo Li (University of California\, San Diego) as part of G
 lobal Seminar on Mathematical Modeling and Applications\n\n\nAbstract\nLig
 and-receptor binding and unbinding are fundamental molecular processes\, a
 nd are particularly essential to drug efficacy\, whereas water fluctuation
 s impact the corresponding thermodynamics and kinetics. We develop a varia
 tional implicit-solvent model (VISM) to calculate the potential of mean fo
 rce (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 r
 ates\, and conduct two-state Brownian dynamics simulations of the ligand s
 tochastic motion\, providing the mean first-passage times for the ligand-p
 ocket binding and unbinding. We find that the dewetting transition around 
 the pocket is slowed down as the ligand approaches the pocket but is peake
 d suddenly once the ligand enters the pocket. In contrast to binding\, the
  ligand unbinding involves a much larger timescale due to a high energy ba
 rrier at the pocket entrance. The dry-wet fluctuation slows down the bindi
 ng but accelerates the unbinding process. Without any explicit description
  of individual water molecules\, our predictions are in a very good\, qual
 itative and semi-quantitative\, agreement with existing explicit-water mol
 ecular dynamics simulations\, providing a promising step in further effici
 ent studies of the ligand-receptor binding/unbinding kinetics. This is joi
 nt work with Shenggao Zhou\, R. Gregor Weiss\, Li-Tien Cheng\, Joachim Dzu
 biella\, and J. Andrew McCammon.\n\ntalk has ended\n
LOCATION:https://researchseminars.org/talk/GSMMA/6/
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