title: SuperStar: a knowledge-based approach for identifying interaction sites in proteins.
abstract: An empirical method for identifying interaction sites in proteins is described and validated. The method is based entirely on experimental information about non-bonded interactions occurring in small-molecule crystal structures. These data are used in the form of scatterplots that show the experimentally observed distribution of one functional group (the "contact group" or "probe") around another. A template molecule (e.g. a protein binding site) is broken down into structure fragments and the scatterplots, showing the distribution of a chosen probe around these structure fragments, are superimposed on the corresponding parts of the template. The scatterplots are then translated into a three-dimensional map that shows the propensity of the probe at different positions around the template molecule. The method is illustrated for l -arabinose-binding protein, complexed with l -arabinose and with d -fucose, and for dihydrofolate reductase complexed with methotrexate. The method is validated on 122 X-ray structures of protein-ligand complexes. For all the binding sites of these proteins, propensity maps are generated for four different probes: a charged NH+3nitrogen, a carbonyl oxygen, a hydroxyl oxygen and a methyl carbon atom. Next, the maps are compared with the experimentally observed positions of ligand atoms of these types. For 74% of these ligand atoms (84% of the solvent-inaccessible ones) the calculated propensity of the matching probe at the experimental positions is higher than expected by chance. For 68% of the atoms (82% of the solvent-inaccessible ones) the propensity of the matching probe is higher than that of the other three probes. These results indicate that the approach generally gives good predictions for protein-ligand interactions. The potential applications of the propensity maps range from an aid in manual docking and structure-based drug design to their use in pharmacophore development.
authors.affiliation: Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK. verdonk@ccdc.cam.ac.uk
authors.forename: M L
authors.initials: ML
authors.lastname: Verdonk
authors.forename: J C
authors.initials: JC
authors.lastname: Cole
authors.forename: R
authors.initials: R
authors.lastname: Taylor
mesh_terms: D001185:Artificial Intelligence; D001665:Binding Sites; D002352:Carrier Proteins / Q000737:chemistry / Q000378:metabolism; D029968:Escherichia coli Proteins; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012986:Software Validation; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry / Q000378:metabolism
chemical_list: C016242:AraF protein, E coli; D002352:Carrier Proteins; D029968:Escherichia coli Proteins; D011506:Proteins; D013762:Tetrahydrofolate Dehydrogenase