Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment

MOTIVATION: 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…

• NS
  National Science Foundation

  Awards: 0746198, DBI 0746198
• NI
  National Institute of General Medical Sciences

  Awards: GM083107, GM083107, GM084222, GM084222