Prediction of SAMPL3 host-guest affinities with the binding energy distribution analysis method (BEDAM)
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BEDAM calculations are described to predict the free energies of binding of a series of anaesthetic drugs to a recently characterized acyclic cucurbituril host. The modeling predictions, conducted as part of the SAMPL3 host-guest affinity blind challenge, are generally in good quantitative agreement with the experimental measurements. The correlation coefficient between computed and measured binding free energies is 70% with high statistical significance. Multiple conformational stereoisomers and protonation states of the guests have been considered. Better agreement is obtained with high statistical confidence under acidic modeling conditions. It is shown that this level of quantitative agreement could have not been reached without taking into account reorganization energy and configurational entropy effects. Extensive conformational variability of the host, the guests and their complexes is observed in the simulations, affecting binding free energy estimates and structural predictions. A conformational reservoir technique is introduced as part of the parallel Hamiltonian replica exchange molecular dynamics BEDAM protocol to fully capture conformational variability. It is shown that these advanced computational strategies lead to converged free energy estimates for these systems, offering the prospect of utilizing host-guest binding free energy data for force field validation and development.
KeywordsBEDAM AGBNP Binding free energy Host-guest SAMPL3 Replica exchange
This work has been supported in part by a research grant from the National Institute of Health (GM30580). The calculations reported in this work have been performed at the BioMaPS High Performance Computing Center at Rutgers University funded in part by the NIH shared instrumentation grants no. 1 S10 RR022375 and 1 S10 RR027444, and on the Lonestar4 cluster at the Texas Advanced Computing Center under TeraGrid/XSEDE National Science Foundation allocation grant no. TG-MCB100145.
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