Dataset Information

Application of MM-GB/SA and WaterMap to SRC Kinase Inhibitor Potency Prediction.

ABSTRACT: WaterMap and MM-GB/SA scoring methods were applied to an extensive congeneric series of small-molecule SRC inhibitors with high-quality enzyme data and well characterized binding modes to compare the performance of these scoring methods in this data set and to provide insight into the relative strengths of each method. Only minor conformational changes in SRC bound with representative DFG-in class of inhibitors were demonstrated in previous studies; thus, the protein flexibility that normally presents a challenge to pose and potency predictions was minimized in this model system. While WaterMap correctly recognized major trends in the SAR of this series, MM-GB/SA performed better in ranking the relative ligand affinities. The different scoring methods were further analyzed to determine which aspects of series SAR were more amenable to MM-GB/SA than WaterMap scoring.

SUBMITTER: Kohlmann A

PROVIDER: S-EPMC4025851 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Application of MM-GB/SA and WaterMap to SRC Kinase Inhibitor Potency Prediction.

Kohlmann Anna A Zhu Xiaotian X Dalgarno David D

ACS medicinal chemistry letters 20120106 2

WaterMap and MM-GB/SA scoring methods were applied to an extensive congeneric series of small-molecule SRC inhibitors with high-quality enzyme data and well characterized binding modes to compare the performance of these scoring methods in this data set and to provide insight into the relative strengths of each method. Only minor conformational changes in SRC bound with representative DFG-in class of inhibitors were demonstrated in previous studies; thus, the protein flexibility that normally pr ...[more]

PMID: 24900440

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Project description:BackgroundThe reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes.ResultsWe applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the ab initio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy.ConclusionsOur results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone.

Dataset Information

Application of MM-GB/SA and WaterMap to SRC Kinase Inhibitor Potency Prediction.

Publications

Application of MM-GB/SA and WaterMap to SRC Kinase Inhibitor Potency Prediction.

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