Using Consensus-Shape Clustering to Identify Promiscuous Ligands and Protein targets and to Choose the Right Query for Shape-Based Virtual Screening
Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based “consensus shapes” to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.
Predicting drug polypharmacology using a novel surface property similarity-based approach
In recent years, polypharmacology is becoming an increasingly important aspect in drug design. For example, pharmaceutical companies are discovering more and more cases in which multiple drugs bind to a given target (promiscuous targets) and in which a given drug binds to more than one target (promiscuous ligands). Both of these phenomena are clearly of great importance when considering drug side-effects. Given that screening drugs against all the proteins expressed by the human genome is infeasible, several computational techniques for predicting the pharmacological profiles of drugs have been developed, ranging from statistical analyses of chemical fingerprints and biological activities  to 3D docking of ligand structures into protein pockets.
Here we present a novel shape-based approach which uses spherical harmonic (SH) representations[2,3] to compare molecular surfaces and key surface properties very efficiently. This approach compares targets by the SH similarity of their ligands and also of their binding pockets. This allows promiscuous ligands and targets to be identified and characterized.
In this contribution, we present details of our approach applied to a subset of the MDL Drug Data Report (MDDR) database containing 65367 compounds distributed over 249 diverse pharmacological targets for which experimental binding information is known. The similarity of each ligand to each target’s ligand set is quantified and used to predict promiscuity. To our knowledge, this is the largest all-against-all polypharmacological study to have been carried out using shape-based techniques. We compare our promiscuity predictions with computational and experimental results obtained by Keiseret al.. We also analyse the correlation between binding pocket shapes and ligand-based promiscuity predictions using the ligand and pocket shape similarity matrices.
Using spherical harmonic surface property representations for ligand-based virtual screening
Ligand-based virtual screening (VS) techniques have become well established in the drug discovery process. However, despite their relative success, there still exists the problem of how to define the initial query compounds and which of their conformations should be used. Here, we propose a novel shape plus surface property approach using multiple local spherical harmonic (SH) functions. We also investigate the use of shape-based and shape plus property-based consensus SH queries calculated in several different ways. The utility of these approaches is compared using the 40 pharmaceutically relevant targets of the DUD database. Our results show that using a combination of SH-based properties often gives better VS performance than using simple shape-based queries. Shape-based consensus queries also perform well, but we find that explicit 3D shape-property conformations should be retained for highly flexible ligands.
Applying in silico Tools to the Discovery of Novel CXCR4 Inhibitors
The process of HIV entry begins with the binding of the viral envelope glycoprotein gp120 to both the CD4 receptor and one of the CXCR4 or CCR5 chemokine co-receptors. There is currently considerable interest in developing novel ligands that can bind to these co-receptors and hence block virus-cell fusion. This article reviews the use of different in silico structure-based and ligand-based virtual screening (VS) tools for the discovery of potential HIV entry inhibitors for the CXCR4 receptor. More specifically, it discusses homology modelling, de novo design, docking, QSAR analyses, pharmacophore modelling, and similarity searches. Results from retrospective VS of a library of known CXCR4 inhibitors taken from the literature and from prospective VS of a combinatorial virtual library are reviewed. The structures of active compounds found by these approaches, as well as CXCR4 inhibitors currently in development, are also discussed. Drug Dev Res 72: 95–111, 2011. © 2010 Wiley-Liss, Inc.