Postprocessing of Docked Protein−Ligand Complexes Using Implicit Solvation Models

Abstract

Molecular docking plays an important role in drug discovery as a tool for the structure-based design of small organic ligands for macromolecules. Possible applications of docking are identification of the bioactive conformation of a protein-ligand complex and the ranking of different ligands with respect to their strength of binding to a particular target. We have investigated the effect of implicit water on the postprocessing of binding poses generated by molecular docking using MM-PB/GB-SA (molecular mechanics Poisson-Boltzmann and generalized Born surface area) methodology. The investigation was divided into three parts: geometry optimization, pose selection, and estimation of the relative binding energies of docked protein-ligand complexes. Appropriate geometry optimization afforded more accurate binding poses for 20% of the complexes investigated. The time required for this step was greatly reduced by minimizing the energy of the binding site using GB solvation models rather than minimizing the entire complex using the PB model. By optimizing the geometries of docking poses using the GB(HCT+SA) model then calculating their free energies of binding using the PB implicit solvent model, binding poses similar to those observed in crystal structures were obtained. Rescoring of these poses according to their calculated binding energies resulted in improved correlations with experimental binding data. These correlations could be further improved by applying the postprocessing to several of the most highly ranked poses rather than focusing exclusively on the top-scored pose. The postprocessing protocol was successfully applied to the analysis of a set of Factor Xa inhibitors and a set of glycopeptide ligands for the class II major histocompatibility complex (MHC) A(q) protein. These results indicate that the protocol for the postprocessing of docked protein-ligand complexes developed in this paper may be generally useful for structure-based design in drug discovery.

Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method

“…Although MM‐PBSA calculations can be performed based on single structures, e.g. minimized structures2,16,17 or crystal structures,18 they are typically conducted based on a conformational ensemble generated by molecular dynamics (MD) simulations. In the latter case, each energy component is determined by averaging over the respective energy contributions from all conformers in the ensemble.…”

“…However, simple scoring functions may neglect important energetic contributions, such as the solvation free energy, and, thus, may have difficulties in correctly predicting the binding pose and binding affinity of a molecule. A combination of docking, molecular mechanics calculations, and MM‐PBSA proved to be more successful in the identification of the correct ligand binding pose than pure docking approaches in several cases 2,17,23,24…”

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

“…As a consequence, it is not possible to know a priori if a docking procedure is more suitable than another one for a particular task. Several attempts to increase docking accuracy and reliability, thus improving its VS performance, reported in literature concerned the development of optimized SFs, rescoring procedures and pose filtering methods 13,[25][26][27][28][29][30][31] . Among these strategies, the combination of different scoring functions (consenus scoring) to improve VS hit rates is a well known practice introduced by Charifson et al in 1999 31 that is still commonly applied [33][34][35][36][37][38] .…”

Hierarchical virtual screening approaches in small molecule drug discovery

“…Finally, in vitro assay identified two sub-micromolar compounds with novel chemotypes. To efficiently take advantage of MM-PBSA binding free energy based ranking, computationally inexpensive VS methodologies have been developed [153][154][155]. inactive DFG-out conformations in kinases to achieve selectivity against various kinases or the active state of GPCRs for the identification of agonists.…”