title: Novel statistical-thermodynamic methods to predict protein-ligand binding positions using probability distribution functions.
abstract: We present two novel methods to predict native protein-ligand binding positions. Both methods identify the native binding position as the most probable position corresponding to a maximum of a probability distribution function (PDF) of possible binding positions in a protein active site. Possible binding positions are the origins of clusters composed, on the basis of root-mean square deviations (RMSD), from the multiple ligand positions determined by a docking algorithm. The difference between the methods lies in the ways the PDF is derived. To validate the suggested methods, we compare the averaged RMSD of the predicted ligand docked positions relative to the experimentally determined positions for a set of 135 PDB protein-ligand complexes. We demonstrate that the suggested methods improve docking accuracy by as much as 21-24% in comparison with a method that simply identifies the binding position as the energy top-scored ligand position.
authors.affiliation: Force Field Laboratory, Algodign, LLC, Moscow, Russia. aruvinsky@yahoo.com
authors.forename: A M
authors.initials: AM
authors.lastname: Ruvinsky
authors.forename: A V
authors.initials: AV
authors.lastname: Kozintsev
mesh_terms: D001665:Binding Sites; D008024:Ligands; D008958:Models, Molecular; D008962:Models, Theoretical; D011336:Probability; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012984:Software; D013816:Thermodynamics
chemical_list: D008024:Ligands; D011506:Proteins