title: A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions.
abstract: Knowledge-based scoring functions are widely used for assessing putative complexes in protein-ligand and protein-protein docking and for structure prediction. Even with large training sets, knowledge-based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge-based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge-based scoring function with an alternative, force-field-based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein-ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge-based scoring functions and can also be applied to protein-protein docking and protein structure prediction.
authors.affiliation: Informatics Institute, University of Missouri, Columbia, Missouri, 65211; Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211.
authors.forename: Sam Z
authors.initials: SZ
authors.lastname: Grinter
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zou
mesh_terms: D001499:Bayes Theorem; D008024:Ligands; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry*
chemical_list: D008024:Ligands; D011506:Proteins
keywords: knowledge-based scoring function; ligand interactions; molecular docking; protein; sparse data