Preparation of a Brucella multiepitope fusion protein based on bioinformatics and its application in serological diagnosis of human brucellosis

Abstract

Summary Brucellosis is a zoonotic infectious disease caused by the Brucella spp., and the enhancement of diagnostic techniques is imperative for effective disease control. Currently, the diagnosis of brucellosis predominantly relies on serological tests, bacterial culture, and molecular biology methods. Among these approaches, serological diagnosis is the most widely utilized due to its relative simplicity. However, existing diagnostic antigens encounter challenges, such as cross-reactivity. Consequently, the development of novel antigens with high specificity and sensitivity is essential to improve the accuracy and efficiency of serological diagnosis for brucellosis. In this study, five antigenic proteins—Erythritol kinase, Nucleoside diphosphate kinase (NDK), Adenosylhomocysteinase, the 31 kDa immunogenic protein, and Lyso-ornithine lipid O-acyltransferase—were selected, and B-cell linear epitopes were predicted using bioinformatics tools. Four prediction tools, namely ABCpred, SVMTriP, BCPred, and Bepipred Linear Epitope Prediction 2.0, were employed to screen for overlapping candidate epitopes. Fusion proteins were constructed through prokaryotic expression to serve as antigens for serological diagnosis. The sensitivity and specificity of the fusion protein were evaluated using indirect ELISA to detect human IgG antibodies in serum samples. The results indicated that the fusion protein achieved sensitivity and specificity values of 0.8095 and 0.9949, respectively. Although these values were lower in comparison to traditional antigens such as lipopolysaccharide (LPS) and the Rose Bengal antigen, the fusion protein exhibited improved cross-reactivity. This study successfully developed a multiepitope fusion protein for the diagnosis of brucellosis, thereby providing a foundation for the creation of highly specific and sensitive diagnostic antigens.

Keywords

• Brucellosis
• Bioinformatics
• Multiepitope fusion protein
• Indirect ELISA
• Diagnosis