Recognition of outer membrane proteins using multiple feature fusion

• Published in: Frontiers in genetics Vol. 14; p. 1211020
• Main Authors: Su, Wenxia, Qian, Xiaojun, Yang, Keli, Ding, Hui, Huang, Chengbing, Zhang, Zhaoyue
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 07.06.2023
• Subjects: Genetics; jackknife test; outer membrane protein; prediction model; pseudo amino acid composition; support vector machine; prediction model; support vector machine; pseudo amino acid composition; jackknife test; outer membrane protein
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2023.1211020

Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.