Metabolism-directed structure optimization of benzimidazole-based Francisella tularensis enoyl-reductase (FabI) inhibitors

Structural approaches to pathway-specific antimicrobial agents

This perspective provides an overview of the evolution of antibiotic discovery from a largely phenotypic-based effort, through an intensive structure-based design focus, to a more holistic approach today. The current focus on antibiotic development incorporates assay and discovery conditions that replicate the host environment as much as feasible. They also incorporate several strategies, including target identification and validation within the whole cell environment, a variety of target deconvolution methods, and continued refinement of structure-based design approaches.

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.

Determination of absolute configuration and binding efficacy of benzimidazole-based FabI inhibitors through the support of electronic circular dichroism and MM-GBSA techniques

We have previously reported benzimidazole-based compounds to be potent inhibitors of FabI for Francisella tularensis (FtFabI), making them promising antimicrobial hits. Optically active enantiomers exhibit markedly differing affinities toward FtFabI. The IC₅₀ of benzimidazole (-)-1 is ∼100× lower than the (+)-enantiomer, with similar results for the 2 enantiomers. Determining the absolute configuration for these optical compounds and elucidating their binding modes is important for further design. Electronic circular dichroism (ECD) quantum calculations have become important in determining absolute configurations of optical compounds. We determined the absolute configuration of (-)/(+)-1 and (-)/(+)-2 by comparing experimental spectra and theoretical density functional theory (DFT) simulations of ECD spectra at the B3LYP/6-311+G(2d, p) level using Gaussian09. Comparison of experimental and calculated ECD spectra indicates that the S configuration corresponds to the (-)-rotation for both compounds 1 and 2, while the R configuration corresponds to the (+)-rotation. Further, molecular dynamics simulations and MM-GBSA binding energy calculations for these two pairs of enantiomers with FtFabI show much tighter binding MM-GBSA free energies for S-1 and S-2 than for their enantiomers, R-1 and R-2, consistent with the S configuration being the more active one, and with the ECD determination of the S configuration corresponding to (-) and the R configuration corresponding to (+). Thus, our computational studies allow us to assign (-) to (S)- and (+) to (R)- for compounds 1 and 2, and to further evaluate structural changes to improve efficacy.

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

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 parahalogen in benzimidazole compounds might form a weak halogen bond with FabI, which is a well-known halogen bond favoring enzyme.

Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F. tularensis enoyl-ACP reductase (FabI)

To validate a method for predicting the binding affinities of FabI inhibitors, three implicit solvent methods, MM-PBSA, MM-GBSA, and QM/MM-GBSA were carefully compared using 16 benzimidazole inhibitors in complex with Francisella tularensis FabI. The data suggests that the prediction results are sensitive to radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length, if only one simulation trajectory is used for each ligand. In this case, QM/MM-GBSA using 6 ns MD simulation trajectories together with GB(neck2) , PM3, and the mbondi2 radii set, generate the closest agreement with experimental values (r(2)  = 0.88). However, if the three implicit solvent methods are averaged from six 1 ns MD simulations for each ligand (called "multiple independent sampling"), the prediction results are relatively insensitive to all the tested parameters. Moreover, MM/GBSA together with GB(HCT) and mbondi, using 600 frames extracted evenly from six 0.25 ns MD simulations, can also provide accurate prediction to experimental values (r(2)  = 0.84). Therefore, the multiple independent sampling method can be more efficient than a single, long simulation method. Since future scaffold expansions may significantly change the benzimidazole's physiochemical properties (charges, etc.) and possibly binding modes, which may affect the sensitivities of various parameters, the relatively insensitive "multiple independent sampling method" may avoid the need of an entirely new validation study. Moreover, due to large fluctuating entropy values, (QM/)MM-P(G)BSA were limited to inhibitors' relative affinity prediction, but not the absolute affinity. The developed protocol will support an ongoing benzimidazole lead optimization program.