Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints

• DOI: 10.1021/ci300200r
• Publication Date: 2013-03-12T19:18:22Z
• Authors: Vladimir Chupakhin; Gilles Marcou; Igor Baskin; Alexandre Varnek; Didier Rognan
• Keywords: 0303 health sciences; Binding Sites; Cyclin-Dependent Kinase 2; Quantitative Structure-Activity Relationship; Crystallography, X-Ray; Ligands; Peptide Mapping; Protein Structure, Secondary; Mitogen-Activated Protein Kinase 14; Molecular Docking Simulation; 03 medical and health sciences; [CHIM.CHEM] Chemical Sciences/Cheminformatics; Humans; HSP90 Heat-Shock Proteins; Neural Networks, Computer; [CHIM.CHEM]Chemical Sciences/Cheminformatics; Protein Binding

We herewith present a novel approach to predict protein-ligand binding modes from the single two-dimensional structure of the ligand. Known protein-ligand X-ray structures were converted into binary bit strings encoding protein-ligand interactions. An artificial neural network was then set up to first learn and then predict protein-ligand interaction fingerprints from simple ligand descriptors. Specific models were constructed for three targets (CDK2, p38-α, HSP90-α) and 146 ligands for which protein-ligand X-ray structures are available. These models were able to predict protein-ligand interaction fingerprints and to discriminate important features from minor interactions. Predicted interaction fingerprints were successfully used as descriptors to discriminate true ligands from decoys by virtual screening. In some but not all cases, the predicted interaction fingerprints furthermore enable to efficiently rerank cross-docking poses and prioritize the best possible docking solutions.