A First Computational Frame for Recognizing Heparin-Binding Protein

Wen Zhu; Shi-Shi Yuan; Jian Li; Cheng-Bing Huang; Hao Lin; Bo Liao

doi:10.3390/diagnostics13142465

A First Computational Frame for Recognizing Heparin-Binding Protein

Diagnostics (Basel). 2023 Jul 24;13(14):2465. doi: 10.3390/diagnostics13142465.

Authors

Wen Zhu^{1

2

3}, Shi-Shi Yuan⁴, Jian Li⁵, Cheng-Bing Huang⁶, Hao Lin⁴, Bo Liao^{1

2

3}

Affiliations

¹ Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China.
² Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China.
³ School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China.
⁴ School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
⁵ School of Basic Medical Sciences, Chengdu University, Chengdu 610106, China.
⁶ School of Computer Science and Technology, ABa Teachers University, Chengdu 623002, China.

Abstract

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Keywords: amino acid composition; composition/transition/distribution; dipeptide composition; dipeptide deviation from expected mean; heparin-binding protein; support vector machine.

Grants and funding

This work was funded by the National Nature Science Foundation of China (Grant Nos. 62250028, 61863010, 11926205, 11926412, and 61873076), National Key R&D Program of China (Grant No. 2020YFB2104400), and Natural Science Foundation of Hainan, China (Grant Nos. 121RC538, 119MS036, and 120RC588).