EFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands
Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % in binding site detection and by 5-35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75-78 %, which can be further improved by 3-4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite. © 2013 Springer Science+Business Media Dordrecht.
Publication Source (Journal or Book title)
Journal of Computer-Aided Molecular Design
Brylinski, M., & Feinstein, W. (2013). EFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. Journal of Computer-Aided Molecular Design, 27 (6), 551-567. https://doi.org/10.1007/s10822-013-9663-5