combination of genetic programming and support vector machine-based prediction of protein-peptide binding sites with sequence and structure-based features

Prediction of the peptide-binding site of proteins is a signi cant and essential task in di erent processes such as understanding biological processes, protein functional analysis, comparison of functional sites, comprehension of the transactions mechanism, drug design, cellular signaling, and cancer treatment. predictive analysis of the protein-peptide binding site is one of the most challenging bioinformatics issues. experimental methods are time-consuming, costly, and laborious. therefore, we propose a machine learning-based method for predicting protein-peptide binding sites by utilizing enhanced features vector obtained from three-dimensional protein structure and one-dimensional sequence string data. to this end, the genetic programming technique is applied to the obtained basic features extract a more discriminative feature vector. then support vector machine is employed to determine the binding residue of each amino acid. finally, the binding sites are predicted using the structure clustering algorithm on the obtained binding residues. the proposed method was evaluated on the bio lip dataset. the prediction rate of 92.76% and 93.09% were achieved when 10-fold cross-validation and independent test set respectively used. the acquired results were compared to the performance of other state-of-the-art methods. the proposed method achieves robust and consistent performance using sequence-based and structure-based features for both 10-fold cross-validation and independent tests.