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

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 b...

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Bibliographic Details
Published in:Journal of computational chemistry 2006-11, Vol.27 (15), p.1866-1875
Main Authors: Huang, Sheng-You, Zou, Xiaoqin
Format: Article
Language:English
Subjects:
Chemical reactions
Expert systems
knowledge-based
ligand binding
Ligands
Models, Chemical
Molecular structure
Protein Binding
protein-ligand interactions
Proteins
Proteins - chemistry
Proteins - metabolism
scoring function
statistical potentials
Studies
Thermodynamics
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Summary: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. © 2006 Wiley Periodicals, Inc. J Comput Chem, 2006
ISSN:0192-8651
1096-987X
DOI:10.1002/jcc.20504