Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i>
Background: <i>Nodaviridae</i> 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. <i>Nodaviridae</i> are categorized into three subfamilies: alphanodavi...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/14/7/1357 |
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| author | Tao-Chuan Shih Li-Ping Ho Hsin-Yiu Chou Jen-Leih Wu Tun-Wen Pai |
| author_facet | Tao-Chuan Shih Li-Ping Ho Hsin-Yiu Chou Jen-Leih Wu Tun-Wen Pai |
| author_sort | Tao-Chuan Shih |
| collection | DOAJ |
| description | Background: <i>Nodaviridae</i> 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. <i>Nodaviridae</i> 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 <i>Nodaviridae</i> family could be identified. The advantages of undocumented cross infection among the different host species for the <i>Nodaviridae</i> 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 <i>Nodaviridae</i>. 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. |
| first_indexed | 2024-03-09T05:44:02Z |
| format | Article |
| id | doaj.art-5313b1b86ad74bed8aebbec8f7366145 |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-09T05:44:02Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-5313b1b86ad74bed8aebbec8f73661452023-12-03T12:22:41ZengMDPI AGViruses1999-49152022-06-01147135710.3390/v14071357Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i>Tao-Chuan Shih0Li-Ping Ho1Hsin-Yiu Chou2Jen-Leih Wu3Tun-Wen Pai4Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Aquaculture, National Penghu University of Science and Technology, Penghu 88046, TaiwanDepartment of Aquaculture, College of Life Science, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, TaiwanBackground: <i>Nodaviridae</i> 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. <i>Nodaviridae</i> 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 <i>Nodaviridae</i> family could be identified. The advantages of undocumented cross infection among the different host species for the <i>Nodaviridae</i> 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 <i>Nodaviridae</i>. 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.https://www.mdpi.com/1999-4915/14/7/1357linear epitope <i>Nodaviridae</i>host specificitymulti-expert prediction |
| spellingShingle | Tao-Chuan Shih Li-Ping Ho Hsin-Yiu Chou Jen-Leih Wu Tun-Wen Pai Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i> Viruses linear epitope <i>Nodaviridae</i> host specificity multi-expert prediction |
| title | Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i> |
| title_full | Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i> |
| title_fullStr | Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i> |
| title_full_unstemmed | Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i> |
| title_short | Comprehensive Linear Epitope Prediction System for Host Specificity in <i>Nodaviridae</i> |
| title_sort | comprehensive linear epitope prediction system for host specificity in i nodaviridae i |
| topic | linear epitope <i>Nodaviridae</i> host specificity multi-expert prediction |
| url | https://www.mdpi.com/1999-4915/14/7/1357 |
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