Assessing the Role of Polarization in Docking

Abstract

We describe a strategy for including ligand and protein polarization in docking that is based on the conversion of induced dipoles to induced charges. Induced charges have a distinct advantage in that they are readily implemented into a number of different computer programs, including many docking programs and hybrid QM/MM programs; induced charges are also more readily interpreted. In this study, the ligand was treated quantum mechanically to avoid parametrization issues and was polarized by the target protein, which was treated as a set of point charges. The induced dipole at a given target atom, due to polarization by the ligand and neighboring residues, was reformulated as induced charges at the given atom and its bonded neighbors, and these were allowed to repolarize the ligand in an iterative manner. The final set of polarized charges was evaluated in docking using AutoDock 4.0 on 12 protein-ligand systems against the default empirical Gasteiger charges, and against nonpolarized and partially polarized potential-derived charges. One advantage of AutoDock is that the best rmsd structure can be identified not only from the lowest energy pose but also from the largest cluster of poses. Inclusion of polarization does not always lead to the lowest energy pose having a lower rmsd, because docking is designed by necessity to be rapid rather than accurate. However, whenever an improvement in methodology, corresponding to a more thorough treatment of polarization, resulted in an increased cluster size, then there was also a corresponding decrease in the rmsd. The options for implementing polarization within a purely classical docking framework are discussed.2008112

Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description

“…Future developments in these areas, coupled with an enhanced description of the physics underlying macromolecular interaction [232,[283][284][285][286][287][288][289][290][291][292][293][294][295][296], will be key for more predictive computational tools in structure-based drug discovery and design. Building the most reliable models is still based, and will probably always be, on the deep knowledge of the system or event under study.…”

LigDockCSA: Protein–ligand docking using conformational space annealing

“…These results imply that a bound state corresponding to a broader energy basin tends to be favored, which is in accordance with recent reports that illustrated the importance of incorporating conformational entropy to generate more precise prediction of binding poses. [28,[44][45][46][47][48][49] Despite the success in the pose prediction, the new method improves the prediction accuracy of binding affinity very little compared to AutoDock because the magnitude of the added torsion term is very small (data not shown).…”

“…[18][19][20][21][22][23] A successful docking method requires an accurate energy function (or scoring function) and an efficient search method. Improvement in either the energy function or the search method used in docking studies can result in improved performance, [13][14][15][16][17][24][25][26][27][28] but simultaneous improvement in both the energy function and the search method would bring the ideal result. In this work, we provide an example in which an efficient search method can identify a loophole in the energy function, and the energy function is refined so that lower energy conformations correspond to more native-like ones.…”

“…[19,20,23,[35][36][37] It has been reported that better optimization of the AutoDock energy leads to more accurate docking predictions. [15][16][17]28] However, we observe that although CSA can sample conformations with lower energy than the Lamarckian genetic algorithm (LGA) [4] of AutoDock, the lower energy conformations tend to be less native-like. Modification of the AutoDock energy by addition of an explicit torsional energy term from the piecewise linear potential (PLP) score [38] resulted in better performance due to relief of torsional strains.…”

Many-body effects and simulations of potassium channels

“…In common with the fluctuating charge model, polarization is here included without the addition of dipoles or additional off-atom charges, potentially allowing for easy incorporation into modelling software. This method has successfully been applied to energy calculations in both small-molecule (Illingworth et al 2006) and enzymatic (Illingworth et al 2008b) systems, and to the problem of ligand docking (Illingworth et al 2008a). …”