Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae

Tao-Chuan Shih; Li-Ping Ho; Hsin-Yiu Chou; Jen-Leih Wu; Tun-Wen Pai

doi:10.3390/v14071357

Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae

Viruses. 2022 Jun 22;14(7):1357. doi: 10.3390/v14071357.

Authors

Tao-Chuan Shih¹, Li-Ping Ho², Hsin-Yiu Chou³, Jen-Leih Wu^{4

5}, Tun-Wen Pai¹

Affiliations

¹ Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
² Department of Aquaculture, National Penghu University of Science and Technology, Penghu 88046, Taiwan.
³ Department of Aquaculture, College of Life Science, National Taiwan Ocean University, Keelung 20224, Taiwan.
⁴ Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung 20224, Taiwan.
⁵ Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 11529, Taiwan.

Abstract

Background: Nodaviridae infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. Nodaviridae are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs).

Methodology: A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the Nodaviridae family could be identified. The advantages of undocumented cross infection among the different host species for the Nodaviridae family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs.

Principal findings: We predicted 29 LEs for Nodaviridae. The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined.

Conclusion: Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.

Keywords: host specificity; linear epitope Nodaviridae; multi-expert prediction.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Antigens
Epitopes
Fishes
Host Specificity
Nodaviridae* / genetics

Substances

Antigens
Epitopes

Grants and funding

This research was funded by Ministry of Science and Technology, Taiwan grant number MOST 107–2313-B-027-001 to T.-W.P., MOST 110-2321-B-019-001 to H.-Y.C., and MOST 109-2321-B-019-005 to J.-L.W. And The APC was funded by MOST 110-2321-B-019-001.