期刊
JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 27, 期 15, 页码 1866-1875出版社
WILEY
DOI: 10.1002/jcc.20504
关键词
scoring function; protein-ligand interactions; ligand binding; knowledge-based; statistical potentials
资金
- NIDDK NIH HHS [K25 DK061529, DK61529] Funding Source: Medline
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. (C) 2006 Wiley Periodicals, Inc.
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