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An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials.

Sheng-You Huang·Xiaoqin Zou
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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.

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LigandsModels, ChemicalProtein BindingProteinsThermodynamicsJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov't

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Sheng-You Huang, Xiaoqin Zou. 2006-11-30. An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials.. https://doi.org/10.1002/jcc.20504

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