title: Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment.
abstract: BACKGROUND
  Identification of protein-ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem.
  
  
  RESULTS
  We develop two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize >51% of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P-value <10(-9) in student t-test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15% over the best individual predictions. COACH was examined in the recent community-wide COMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5% higher than the second best method. These data demonstrate a new robust approach to protein-ligand binding site recognition, which is ready for genome-wide structure-based function annotations.
  
  
  BACKGROUND
  http://zhanglab.ccmb.med.umich.edu/COACH/
authors: Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA.
authors: Jianyi
authors: J
authors: Yang
authors: Ambrish
authors: A
authors: Roy
authors: Yang
authors: Y
authors: Zhang
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D030562:Databases, Protein; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D008024:Ligands; D011506:Proteins
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