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

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. © 2006 Wiley Periodicals, Inc. J Comput Chem, 2006