Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement

Abstract

Proteins perform functions through interacting with other molecules. However, structural details for most of the protein-ligand interactions are unknown. We present a comparative approach (COFACTOR) to recognize functional sites of protein-ligand interactions using low-resolution protein structural models, based on a global-to-local sequence and structural comparison algorithm. COFACTOR was tested on 501 proteins, which harbor 582 natural and drug-like ligand molecules. Starting from I-TASSER structure predictions, the method successfully identifies ligand-binding pocket locations for 65% of apo receptors with an average distance error 2 Å. The average precision of binding-residue assignments is 46% and 137% higher than that by FINDSITE and ConCavity. In CASP9, COFACTOR achieved a binding-site prediction precision 72% and Matthews correlation coefficient 0.69 for 31 blind test proteins, which was significantly higher than all other participating methods. These data demonstrate the power of structure-based approaches to protein-ligand interaction predictions applicable for genome-wide structural and functional annotations.