Identifying Dipeptidyl Peptidase-IV Inhibitory Peptides Based on Correlation Information of Physicochemical Properties

Dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides play a crucial role in drug development and the treatment of diabetes. Thus, it is an urgent task to fast and precise distinguishing DPP-IV inhibitory peptides from non-DPP-IV inhibitory peptides. This study developed a support vector machine (SVM) based model to accurately identify DPP-IV inhibitory peptides. Specifically, the peptide sequences were firstly encoded by fifty kinds of physicochemical properties, and dynamic time warping algorithm was introduced to capture the correlation information of distinct physicochemical properties of amino acids. To further remove the effect of noise, orthogonal minimum spanning tree algorithm was proposed. Finally, the features were inputted into SVM to discriminate DPP-IV from non-DPP-IV inhibitory peptides. In the jackknife test, our proposed method achieved 86.28% and 87.97% classification accuracies on benchmark and independent datasets, respectively. The experimental results showed that the proposed method achieved significant improvement in classification performance, as compared with the existing method. The datasets and code are publicly available at https://figshare.com/articles/online_resource/iDPPIV/14769174.