In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking

Abstract

Representing receptors as ensembles of protein conformations during docking is a powerful method to approximate protein flexibility and increase the accuracy of the resulting ranked list of compounds. Unfortunately, docking compounds against a large number of ensemble members can increase computational cost and time investment. In this article, we present an efficient method to evaluate and select the most contributive ensemble members prior to docking for targets with a conserved core of residues that bind a ligand moiety. We observed that ensemble members that preserve the geometry of the active site core are most likely to place ligands in the active site with a conserved orientation, generally rank ligands correctly and increase interactions with the receptor. A relative distance approach is used to quantify the preservation of the three-dimensional interatomic distances of the conserved ligand-binding atoms and prune large ensembles quickly. In this study, we investigate dihydrofolate reductase as an example of a protein with a conserved core; however, this method for accurately selecting relevant ensemble members a priori can be applied to any system with a conserved ligand-binding core, including HIV-1 protease, kinases, and acetylcholinesterase. Representing a drug target as a pruned ensemble during in silico screening should increase the accuracy and efficiency of high-throughput analyses of lead analogs.

Figure 1

Atoms used in the relative difference calculation of LcDHFR, showing the conserved orientation of the 2,4-diaminopyrimidine ring within the active site, with hydrogen bonding defined by crystallographic data.

Figure 2

Representative structures of backbone location variation as determined by tube radius and color (blue to red) with the script average3d.py for 1LUD (left) and 1YHO (right).

Figure 3

The relative distances used in the LcDHFR (left) and hDHFR (right) calculation, with residue numbering as per 3DFR (LcDHFR) and 1KMV (hDHFR).

Figure 4

A comparison of clustering patterns using a relative difference calculation on an ensemble set. A) Geno3D 1ZDR Wholemin shows a clear clustering pattern, b) the Multiple Template calculation shows small regions of defined clusters (RS is very low scoring, and then an obvious cluster of three is seen). The data points corresponding to selected relative difference clusters are shown as open circles. C) The 1LUD_av 3.5Å radius ensemble calculation shows no apparent clustering, with a steady increase across the ensemble. D) Swiss 1ZDR also shows no clustering. RS is low scoring, followed by a smooth gradient of all other ensemble members.

Figure 5

The conserved core of HIV-1 protease. HIV-1 protease is shown with crystal structures and varying ligands (PDB IDs 1T7I, 2AOC, 2FGV, 2HB3, 2HS1, 2I4U, 2O4L, 3B7V64) overlaid with the highly conserved residues Asp 25, Gly 27, Ala 28, Glu 29, and Gly 49 shown in white and ligands superposed in blue.