Evaluating thermodynamic integration performance of the new amber molecular dynamics package and assess potential halogen bonds of enoyl-ACP reductase (FabI) benzimidazole inhibitors

Abstract

Thermodynamic integration (TI) can provide accurate binding free energy insights in a lead optimization program, but its high computational expense has limited its usage. In the effort of developing an efficient and accurate TI protocol for FabI inhibitors lead optimization program, we carefully compared TI with different Amber molecular dynamics (MD) engines (sander and pmemd), MD simulation lengths, the number of intermediate states and transformation steps, and the Lennard-Jones and Coulomb Softcore potentials parameters in the one-step TI, using eleven benzimidazole inhibitors in complex with Francisella tularensis enoyl acyl reductase (FtFabI). To our knowledge, this is the first study to extensively test the new AMBER MD engine, pmemd, on TI and compare the parameters of the Softcore potentials in the one-step TI in a protein-ligand binding system. The best performing model, the one-step pmemd TI, using 6 intermediate states and 1 ns MD simulations, provides better agreement with experimental results (RMSD = 0.52 kcal/mol) than the best performing implicit solvent method, QM/MM-GBSA from our previous study (RMSD = 3.00 kcal/mol), while maintaining similar efficiency. Briefly, we show the optimized TI protocol to be highly accurate and affordable for the FtFabI system. This approach can be implemented in a larger scale benzimidazole scaffold lead optimization against FtFabI. Lastly, the TI results here also provide structure-activity relationship insights, and suggest the para-halogen in benzimidazole compounds might form a weak halogen bond with FabI, which is a well-known halogen bond favoring enzyme.

Thermodynamic integration network approach to ion transport through protein channels: Perspectives and limits

“…In this study, interpolating the interactions has been conducted simultaneously for the charge and the dispersive interactions, which is the recommended approach and was well validated in terms of accuracy and computational performance . During our simulations with the structure of the DH conformation, the Lennard–Jones soft core potential energy function curvature control parameter α was set to 0.5.…”

Rapid and accurate structure‐based therapeutic peptide design using GPU accelerated thermodynamic integration

“…TI and free energy perturbation (FEP) are currently among the most accurate computational techniques for free energy calculation. They have been consistently shown to match experimental binding free energy values with minimal error . Drug–target, protein–protein, protein–DNA, and protein–peptide interaction energies have all been accurately predicted using TI.…”

“…They have been consistently shown to match experimental binding free energy values with minimal error. [11][12][13][14] Drug-target, 12 proteinprotein, 15 protein-DNA, 16 and protein-peptide 17,18 interaction energies have all been accurately predicted using TI. Until recently, performing anything more than a very small number of TI calculations was prohibitively resource intensive.…”

Fast and flexible gpu accelerated binding free energy calculations within the amber molecular dynamics package

“…Previous work by several groups has shown the CPU implementation of TI to be already capable of predicting experimental free energies . Thus for the purposes of validating our GPU implementation we consider values that agree within statistical error to the CPU implementation to indicate success.…”