GalaxySite: ligand-binding-site prediction by using molecular docking

TLDR

Ligand‑binding site knowledge is essential for functional studies, drug design, and protein design, and recent methods demonstrate that incorporating information from structurally similar proteins improves prediction accuracy. GalaxySite is a freely available web server that integrates homologous protein information with molecular docking to predict non‑metal ligand binding sites and also outputs optimized 3D protein–ligand complex coordinates and binding poses, unlike other methods that only predict residue identities or copy geometry. Critical assessments in 2010 and 2012 showed GalaxySite to be superior or comparable to other state‑of‑the‑art ligand‑binding‑site predictors, and its detailed binding geometry predictions are valuable for functional studies and computer‑aided drug discovery.

Abstract

Knowledge of ligand-binding sites of proteins provides invaluable information for functional studies, drug design and protein design. Recent progress in ligand-binding-site prediction methods has demonstrated that using information from similar proteins of known structures can improve predictions. The GalaxySite web server, freely accessible at http://galaxy.seoklab.org/site, combines such information with molecular docking for more precise binding-site prediction for non-metal ligands. According to the recent critical assessments of structure prediction methods held in 2010 and 2012, this server was found to be superior or comparable to other state-of-the-art programs in the category of ligand-binding-site prediction. A strong merit of the GalaxySite program is that it provides additional predictions on binding ligands and their binding poses in terms of the optimized 3D coordinates of the protein–ligand complexes, whereas other methods predict only identities of binding-site residues or copy binding geometry from similar proteins. The additional information on the specific binding geometry would be very useful for applications in functional studies and computer-aided drug discovery.