Binding mode information improves fragment docking

• Published in: Journal of cheminformatics 2019-03, Vol.11 (1), p.24-15, Article 24
• Main Authors: Jacquemard, Célien, Drwal, Malgorzata N., Desaphy, Jérémy, Kellenberger, Esther
• Format: Article
• Language: English
• Subjects: Banks (Finance); Binding; Chemical Sciences; Cheminformatics; Chemistry; Chemistry and Materials Science; Comparative analysis; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Coordination compounds; Crystal structure; Crystallography; Data banks; Docking; Docking pose; Documentation and Information in Chemistry; Drug discovery; Fragment-based drug design (FBDD); Fragmentation; Fragments; Ligand/protein complex; Ligands; Matching; Post-processing; Proceedings of the 11th International Conference on Chemical Structures; Protein binding; Protein structure; Proteins; Research Article; Scoring; Shape recognition; Theoretical and Computational Chemistry
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-019-0346-7

Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.