GRaSP is a residue centric method to predict ligand biding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level.

How does GRaSP calculate the residue neighborhood?

For each residue, physicochemical and topological properties of its atoms and non-covalent interactions are modeled as a graph which, in turn, is encoded as a feature vector. A set of feature vectors is the input for the machine learning predictor.

How does GRaSP predict residue binding sites?

GRaSP uses the residues environment, modeled as feature vectors, as input to a machine learning strategy. The prediction is performed using a balancing strategy to reduce the imbalanced distribution of classes.


Charles A. Santana, Sabrina de A. Silveira, João P. A. Moraes, Sandro C. Izidoro, Raquel C. de Melo-Minardi, António J. M. Ribeiro, Jonathan D. Tyzack, Neera Borkakoti and Janet M. Thornton. GRaSP: a graph-based residue neighborhood strategy to predict binding sites. Bioinformatics (2020).