title: 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.
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA.
authors.forename: Ambrish
authors.initials: A
authors.lastname: Roy
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D008024:Ligands; D011506:Proteins