A simple iterative method to optimize protein–ligand-binding residue prediction

Abstract

This article describes an iterative method (IM) for improving protein–ligand-binding residue prediction. Through modifying the binding residue definition in every iteration, this method, step by step, increased the performance of the classifiers used. Using a balanced assessment index (BAI), the classifier optimized by the IM achieved a value of 80.4 that is bigger than the one (66.9) of the initial classifier. According to mean per-instance BAI scores, a direct comparison of methods has been carried out along with an analysis of statistical significance of the differences in performance. The results show that the iterative method (IM) does achieve a higher mean score than the threshold-altering method (TAM) used in our previous study and there is a statistically significant difference between the two methods. The IM has a significant advantage that it is independent of the concrete residue characterization models and learning algorithms, and more extensively applicable. These results indicate that optimizing the binding residue definition is also an effective approach to improve protein–ligand-binding residue prediction.