GRaSP: a graph-based residue neighborhood strategy to predict binding sites

• Published in: Bioinformatics 2020-12, Vol.36 (Supplement_2), p.i726-i734
• Main Authors: Santana, Charles A, Silveira, Sabrina de A, Moraes, João P A, Izidoro, Sandro C, de Melo-Minardi, Raquel C, Ribeiro, António J M, Tyzack, Jonathan D, Borkakoti, Neera, Thornton, Janet M
• Format: Article
• Language: English
• Subjects: Binding Sites; Hand Strength; Ligands; Proteins; Software
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btaa805

Abstract Motivation The discovery of protein–ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein–ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. Results We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10–20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2–5 h on average. Availability and implementation The source code and datasets are available at https://github.com/charles-abreu/GRaSP. Supplementary information Supplementary data are available at Bioinformatics online.