Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 60, Issue 11, Pages 5340-5352Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.9b00968
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Funding
- NIH [GM31749]
- University of California San Diego
- NIH Molecular Biophysics Training Program [T32-GM008326]
- National Biomedical Computation Resource (NBCR) NIH [P41-GM103426]
- National Science Foundation through XSEDE supercomputing resources [TG-CHE060073]
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To improve lead optimization efforts in finding the right ligand, pharmaceutical industries need to know the ligand's binding kinetics, such as binding and unbinding rate constants, which often correlate with the ligand's efficacy in vivo. To predict binding kinetics efficiently, enhanced sampling methods, such as milestoning and the weighted ensemble (WE) method, have been used in molecular dynamics (MD) simulations of these systems. However, a comparison of these enhanced sampling methods in ranking ligands has not been done. Hence, a WE approach called the concurrent adaptive sampling (CAS) algorithm that uses MD simulations was used to rank seven ligands for beta-cyclodextrin, a system in which a multiscale milestoning approach called simulation enabled estimation of kinetic rates (SEEKR) was also used, which uses both MD and Brownian dynamics simulations. Overall, the CAS algorithm can successfully rank ligands using the unbinding rate constant k(off) values and binding free energy Delta G values, as SEEKR did, with reduced computational cost that is about the same as SEEKR. We compare the CAS algorithm simulations with different parameters and discuss the impact of parameters in ranking ligands and obtaining rate constant and binding free energy estimates. We also discuss similarities and differences and advantages and disadvantages of SEEKR and the CAS algorithm for future use.
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