Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics
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We present a method to identify small molecule ligand binding sites and poses within a given protein crystal structure using GPU-accelerated Hamiltonian replica exchange molecular dynamics simulations. The Hamiltonians used vary from the physical end state of protein interacting with the ligand to an unphysical end state where the ligand does not interact with the protein. As replicas explore the space of Hamiltonians interpolating between these states, the ligand can rapidly escape local minima and explore potential binding sites. Geometric restraints keep the ligands from leaving the vicinity of the protein and an alchemical pathway designed to increase phase space overlap between intermediates ensures good mixing. Because of the rigorous statistical mechanical nature of the Hamiltonian exchange framework, we can also extract binding free energy estimates for all putative binding sites. We present results of this methodology applied to the T4 lysozyme L99A model system for three known ligands and one non-binder as a control, using an implicit solvent. We find that our methodology identifies known crystallographic binding sites consistently and accurately for the small number of ligands considered here and gives free energies consistent with experiment. We are also able to analyze the contribution of individual binding sites to the overall binding affinity. Our methodology points to near term potential applications in early-stage structure-guided drug discovery.
KeywordsLigand binding Binding site identification Binding mode prediction GPU-accelerated molecular dynamics Hamiltonian replica exchange Free energy calculation
We would like to acknowledge support from Teragrid/XSEDE Grant TG-MCB100015 for allocations on the Lincoln and Forge GPU computing clusters, both housed at NCSA at University of Illinois, Urbana-Champaign, as well as partial support from NSF-CBET 1134256. We would also like to thank Peter Eastman, Mark Friedrichs, Randy Radmer, Chris Bruns, and Vijay Pande (Stanford University) for help with OpenMM implementation details within YANK. We would also like to acknowledge David Molbey (UC-Irvine) and Brian Schoichet (University of Toronto) for feedback on aspects of the study.
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