FlexS:  A Method for Fast Flexible Ligand Superposition

Abstract

If no structural information about a particular target protein is available, methods of rational drug design try to superimpose putative ligands with a given reference, e.g., an endogenous ligand. The goal of such structural alignments is, on the one hand, to approximate the binding geometry and, on the other hand, to provide a relative ranking of the ligands with respect to their similarity. An accurate superposition is the prerequisite of subsequent exploitation of ligand data by either 3D QSAR analyses, pharmacophore hypotheses, or receptor modeling. We present the automatic method FLEXS for structurally superimposing pairs of ligands, approximating their putative binding site geometry. One of the ligands is treated as flexible, while the other one, used as a reference, is kept rigid. FLEXS is an incremental construction procedure. The molecules to be superimposed are partitioned into fragments. Starting with placements of a selected anchor fragment, computed by two alternative approaches, the remaining fragments are added iteratively. At each step, flexibility is considered by allowing the respective added fragment to adopt a discrete set of conformations. The mean computing time per test case is about 1:30 min on a common-day workstation. FLEXS is fast enough to be used as a tool for virtual ligand screening. A database of typical drug molecules has been screened for potential fibrinogen receptor antagonists. FLEXS is capable of retrieving all ligands assigned to platelet aggregation properties among the first 20 hits. Furthermore, the program suggests additional interesting candidates, likely to be active at the same receptor. FLEXS proves to be superior to commonly used retrieval techniques based on 2D fingerprint similarities. The accuracy of computed superpositions determines the relevance of subsequently performed ligand analyses. In order to validate the quality of FLEXS alignments, we attempted to reproduce a set of 284 mutual superpositions derived from experimental data on 76 protein-ligand complexes of 14 proteins. The ligands considered cover the whole range of drug-size molecules from 18 to 158 atoms (PDB codes: 3ptb, 2er7). The performance of the algorithm critically depends on the sizes of the molecules to be superimposed. The limitations are clearly demonstrated with large peptidic inhibitors in the HIV and the endothiapepsin data set. Problems also occur in the presence of multiple binding modes (e.g., elastase and human rhinovirus). The most convincing results are achieved with small- and medium-sized molecules (as, e.g., the ligands of trypsin, thrombin, and dihydrofolate reductase). In more than half of the entire test set, we achieve rms deviations between computed and observed alignment of below 1.5 A. This underlines the reliability of FLEXS-generated alignments.

Automatic generation of alignments for 3D QSAR analyses

“…We have, however, recently completed a QSAR analysis of 124 structurally diverse antibacterial phenolics where the FBSS alignments were noticeably different from manual fitting, whilst demonstrating superior predictive ability [37]; further such datasets need to be analysed to determine the generality of this behaviour. Finally, while we have focused here on the use of FBSS, several other programs have been designed to align pairs of 3D molecules, and we are currently evaluating the effectiveness of several such programs [9,21,38] for the generation of 3D QSAR models. …”

“…In the work reported here, we have used FBSS alignments as the input to a 3D QSAR procedure, and compared the results with those obtained from conventional manual alignments; alternative approaches to the automated alignment of structures for 3D QSAR are described by Jain et al [5], Parretti et al [20] and Lemmen et al [21], inter alia.…”

Structure Based 3D-QSAR Studies on Cholinesterase Inhibitors

“…For BuChE, this step was performed by using an incremental construction algorithm and a scoring function based on intermolecular interactions and overlapping density functions implemented in the Flexible Superposition (FlexS) technique (Lemmen et al, 1998). The minimum volume overlap was set at 0.6 and the number of alignments per ligand was used initially as 30 (default) but was changed for the cases where optimum alignment was not obtained.…”

Sublinear ligand-based virtual screening using bitmap indices

“…Screening experiments on literature data show that TrixS BMI obtains comparable hit rates and enrichment values to standard alignment-based virtual screening tools like ROCS [2] and FlexS [3]. Computing times are in the range of 7 to 8 compounds per second in case of searching with drug-like target molecules.…”