We present the Simulation Enabled Estimation of Kinetic Rates (SEEKR) package, a suite of open-source scripts and tools designed to enable researchers to perform multi-scale computation of the kinetics of molecular binding, unbinding, and transport using a combination of molecular dynamics, Brownian dynamics, and milestoning theory. To demonstrate its utility, we compute the kon, koff, and ΔGbind for the protein trypsin with its noncovalent binder, benzamidine, and examine the kinetics and other results generated in the context of the new software, and compare our findings to previous studies performed on the same system. We compute a kon estimate of 2.1±0.3•107 M−1s−1, a koff estimate of 83±14 s−1, and a ΔGbind of −7.4±0.2 kcal•mol−1, all of which compare closely to the experimentally measured values of 2.9•107 M−1s−1, 600±300 s−1, and −6.7 kcal•mol−1, respectively.

Efficient prediction and ranking of small molecule binders by their kinetic ( k and k) and thermodynamic ( Δ G) properties can be a valuable metric for drug lead optimization, as these quantities are often indicators of in vivo efficacy. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict k's, k's, and Δ G's. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, β-cyclodextrin, based on predicted k's and k's. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long time scale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by k and Δ G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish "best practices" for its future use.

<p>Accurate and efficient computational predictions of ligand binding kinetics can be useful to inform drug discovery campaigns, particularly in the screening and lead optimization phases. Simulation Enabled Estimation of Kinetic Rates, SEEKR, is a multiscale molecular dynamics, Brownian dynamics, and milestoning simulation approach for calculating receptor-ligand association and dissociation rates. Here we present the implementation of a Markovian milestoning with Voronoi tessellations approach that significantly reduces the simulation cost of calculations as well as further improving their parallelizability. The new approach is applied to a host-guest system to assess its effectiveness for rank-ordering compounds by kinetic rates and to the model protein system, trypsin, with the noncovalent inhibitor benzamidine. For both applications, we demonstrate that the new approach requires up to a factor of 10 less simulation time to achieve results with comparable or increased accuracy.</p>

No abstract

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 β-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 ΔG 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.