Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms

In bioinformatics, it remains challenging to predict important amino acid residues for the binding amino acid residues regions and to perform binding region-based protein interactions. The present method focused on predicting protein-peptide binding amino acid residues regions using various distinct feature groups. Therefore, we employed machine learning methods to predict the protein-peptide binding amino acid residues and protein-peptide binding amino acid residues regions. Thus, predicting peptide-binding aminoacid residues regions computationally is useful to improve the efficiency and cost-effectiveness of experimental methods. The proposed method has three phases:pre-processing with normalization, processing with classification algorithm, and post-processing with a clustering algorithm. The proposed machine learning method of SVM+OPTICS achieves robust and consistent results for the prediction of protein-peptide-binding amino acid residues regions in terms of amino acid residues and regions.