tetano
Editor, Senior Moderator
Nucleic Acids Res
. 2021 May 1;gkab279.
doi: 10.1093/nar/gkab279. Online ahead of print.
Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning
Edison Ong[SUP] 1 [/SUP], Michael F Cooke[SUP] 2 3 [/SUP], Anthony Huffman[SUP] 1 [/SUP], Zuoshuang Xiang[SUP] 4 [/SUP], Mei U Wong[SUP] 4 [/SUP], Haihe Wang[SUP] 5 [/SUP], Meenakshi Seetharaman[SUP] 3 [/SUP], Ninotchka Valdez[SUP] 3 [/SUP], Yongqun He[SUP] 1 4 6 [/SUP]
Affiliations
Abstract
Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.
. 2021 May 1;gkab279.
doi: 10.1093/nar/gkab279. Online ahead of print.
Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning
Edison Ong[SUP] 1 [/SUP], Michael F Cooke[SUP] 2 3 [/SUP], Anthony Huffman[SUP] 1 [/SUP], Zuoshuang Xiang[SUP] 4 [/SUP], Mei U Wong[SUP] 4 [/SUP], Haihe Wang[SUP] 5 [/SUP], Meenakshi Seetharaman[SUP] 3 [/SUP], Ninotchka Valdez[SUP] 3 [/SUP], Yongqun He[SUP] 1 4 6 [/SUP]
Affiliations
- PMID: 34009334
- DOI: 10.1093/nar/gkab279
Abstract
Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.