FLEXS: a method for fast flexible ligand superposition.
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.
Lemmen
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Institute for Algorithms and Scientific Computing (SCAI), German National Research Center for Information Technology (GMD), Schlobeta Birlinghoven, 53754 Sankt Augustin, Germany. Lemmen@gmd.de
Lengauer
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T
Klebe
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D000465:Algorithms; D001665:Binding Sites; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D013329:Structure-Activity Relationship
D008024:Ligands