New and fast statistical‐thermodynamic method for computation of protein‐ligand binding entropy substantially improves docking accuracy

Abstract

We present a novel method to estimate the contributions of translational and rotational entropy to protein-ligand binding affinity. The method is based on estimates of the configurational integral through the sizes of clusters obtained from multiple docking positions. Cluster sizes are defined as the intervals of variation of center of ligand mass and Euler angles in the cluster. Then we suggest a method to consider the entropy of torsional motions. We validate the suggested methods on a set of 135 PDB protein-ligand complexes by comparing the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock docking program, thus reducing the percent of incorrectly docked ligands by 1.4-fold to four-fold, so that in some cases the percent of ligands correctly docked to within an RMSD of 2 A is above 90%. We show that the suggested method to account for entropy of relative motions is identical to the method based on the Monte Carlo integration over intervals of variation of center of ligand mass and Euler angles in the cluster.

Assessing the Role of Polarization in Docking

“…In this respect, the clustering of docked poses as implemented in AutoDock is useful, as an increase in the size of the largest cluster tends to be associated with an improvement in rmsd. 63,64 It is difficult to see why this is true but the corollary is easier to illustrate in that if the ligand-protein complementarity is poorly represented, then the ligand is likely to dock to multiple locations besides the correct one, as there is no reason the poses should be centered on the native binding site. Thus, Figure 3a shows the location of the lowest energy poses in each of the 8 clusters in the docking of 1-imidazole to cytochrome c peroxidase (1AET) with Gasteiger charges.…”

Structure-Activity Relationship Studies of Fostriecin, Cytostatin, and Key Analogs, with PP1, PP2A, PP5, and (β12–β13)-Chimeras (PP1/PP2A and PP5/PP2A), Provide Further Insight into the Inhibitory Actions of Fostriecin Family Inhibitors

“…Each cluster represents a group of similar binding poses, the degree of similarity being dependent upon the root mean square deviation cutoff. These clusters can be thought of as representing a binding mode plus snapshots of the associated relative internal motions of the receptor-ligand complex (Ruvinsky and Kozintsev, 2005).…”

Protein Ligand Docking in Drug Discovery

“…Tests with different scoring functions (Autodock, D-Score, LigScore, PLP, LUDI, F-Score, ChemScore, X-Score, PMF, DrugScore, etc.) have shown some small improvement in the docking accuracy [219,220].…”

“…Incorporating such effects in an efficient scoring function is presently an important challenge in the field of protein ligand docking. While some attempts to incorporate entropy in scoring functions have been reported in the literature (particularly in knowledge-based scoring functions) [207], most approaches involve re-scoring schemes [218][219][220].…”

“…One way to estimate the translational, rotational and torsional entropy in docking, was developed by Ruvinsky and Kozintsev [220]. In their approach, multiple docking experiments are performed and the results are clustered by similarity.…”