iDPPIV-SI: identifying dipeptidyl peptidase IV inhibitory peptides by using multiple sequence information

Abstract

Currently, diabetes has become a great threaten for people’s health in the world. Recent study shows that dipeptidyl peptidase IV (DPP-IV) inhibitory peptides may be a potential pharmaceutical agent to treat diabetes. Thus, there is a need to discriminate DPP-IV inhibitory peptides from non-DPP-IV inhibitory peptides. To address this issue, a novel computational model called iDPPIV-SI was developed in this study. In the first, 50 different types of physicochemical (PC) properties were employed to denote the peptide sequences. Three different feature descriptors including the 1-order, 2-order correlation methods and discrete wavelet transform were applied to collect useful information from the PC matrix. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was employed to select these most discriminative features. All of these chosen features were fed into support vector machine (SVM) for identifying DPP-IV inhibitory peptides. The iDPPIV-SI achieved 91.26% and 98.12% classification accuracies on the training and independent dataset, respectively. There is a significantly improvement in the classification performance by the proposed method, as compared with the state-of-the-art predictors. The datasets and MATLAB codes (based on MATLAB2015b) used in current study are available at https://figshare.com/articles/online_resource/iDPPIV-SI/20085878.

Communicated by Ramaswamy H. Sarma

Keywords:

• Dipeptidyl peptidase IV inhibitory peptides
• physicochemical properties
• correlation methods
• discrete wavelet transform
• support vector machine

Authors’ Contribution

Hongliang Zou: Conceptualization, Methodology, Data curation, Writing-original draft, preparation, Visualization, Investigation, Validation, Writing-review & editing.

Disclosure Statement

The author declares that there is no conflict of interest.

Data Availability Statement

The datasets and source code of this study can be downloaded via https://figshare.com/articles/online_resource/iDPPIV-SI/20085878.

Additional information

Funding

This work was supported by the Youth Project of Jiangxi Education Department (GJJ2201350).