A Bayesian statistical approach of improving knowledge‐based scoring functions for protein–ligand interactions. Issue 12 (13th March 2014)

• Record Type: Journal Article
• Authors: Grinter, Sam Z.; Zou, Xiaoqin
• Is Part Of: Journal of computational chemistry. Volume 35:Issue 12(2014)
• Page Start: 932
• Page End: 943
• Publication Date: 2014-03-13
• Subjects: Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.23579
• Languages: English
• ISSNs: 0192-8651

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>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. © 2014 Wiley Periodicals, Inc.</p> </abstract>