title: An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials.
abstract: Using a novel iterative method, we have developed a knowledge-based scoring function (ITScore) to predict protein-ligand interactions. The pair potentials for ITScore were derived from a training set of 786 protein-ligand complex structures in the Protein Data Bank. Twenty-six atom types were used based on the atom type category of the SYBYL software. The iterative method circumvents the long-standing reference state problem in the derivation of knowledge-based scoring functions. The basic idea is to improve pair potentials by iteration until they correctly discriminate experimentally determined binding modes from decoy ligand poses for the ligand-protein complexes in the training set. The iterative method is efficient and normally converges within 20 iterative steps. The scoring function based on the derived potentials was tested on a diverse set of 140 protein-ligand complexes for affinity prediction, yielding a high correlation coefficient of 0.74. Because ITScore uses SYBYL-defined atom types, this scoring function is easy to use for molecular files prepared by SYBYL or converted by software such as BABEL.
authors.affiliation: Department of Biochemistry, Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211, USA.
authors.forename: Sheng-You
authors.initials: SY
authors.lastname: Huang
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zou
mesh_terms: D008024:Ligands; D008956:Models, Chemical; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D013816:Thermodynamics
chemical_list: D008024:Ligands; D011506:Proteins