A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction.

ABSTRACT:

Background

Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a mor

SUBMITTER: Cheng T 

PROVIDER: S-EPMC2868011 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature