Combining Biochemical Features and Evolutionary Information for Predicting DNA-Binding Residues in Protein Sequences

Abstract

This paper describes a new machine learning approach for prediction of DNA-binding residues from protein sequence data. Several biologically relevant features, including biochemical properties of amino acid residues and evolutionary information of protein sequences, were selected for input encoding. The evolutionary information was represented as position-specific scoring matrices (PSSMs) and several new descriptors developed in this study. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information together with biochemical features was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset. The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies.KeywordsDNA-binding site predictionfeature extractionevolutionary informationrandom forestsmachine learning