It
is widely accepted that drug–target association and dissociation
rates directly affect drug efficacy and safety. To rationally optimize
drug binding kinetics, one must know the atomic arrangement of the
protein–ligand complex during the binding/unbinding process
in order to detect stable and metastable states. Whereas experimental
approaches can determine kinetic constants with fairly good accuracy,
computational approaches based on molecular dynamics (MD) simulations
can deliver the atomistic details of the unbinding process. Furthermore,
they can also be utilized prospectively to predict residence time
(i.e., the inverse of unbinding kinetics constant, k
off) with an acceptable level of accuracy. Here, we report
a novel method based on adiabatic bias MD with an electrostatics-like
collective variable (dubbed elABMD) for sampling protein–ligand
dissociation events in two kinases. elABMD correctly ranked a ligand
series on glucokinase, in agreement with experimental data and previous
calculations. Subsequently, we applied the new method prospectively
to a congeneric series of GSK-3β inhibitors. For this series,
new crystal structures were generated and the residence time was experimentally
measured with surface plasmon resonance (SPR). There was good agreement
between computational predictions and experimental measures, suggesting
that elABMD is an innovative and efficient tool for calculating residence
times.
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