Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
School of Information, University of Michigan, Ann Arbor, MI 48109, USA.
Nucleic Acids Res. 2021 Jul 2;49(W1):W671-W678. doi: 10.1093/nar/gkab279.
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.
疫苗接种是医学领域最重大的发明之一。反向疫苗学（RV）是一种从病原体基因组预测疫苗候选物的最先进技术。为了促进疫苗开发，我们更新了 Vaxign2，这是第一个使用基于反向疫苗学和机器学习的网络疫苗设计程序。Vaxign2 是一个综合性的网络服务器，用于合理的疫苗设计，包括预测和计算工作流程组件。预测部分包括原始的基于 Vaxign 过滤的方法和一种新的基于机器学习的方法，Vaxign-ML。使用验证数据集进行的基准测试结果表明，与其他 RV 工具相比，Vaxign-ML 具有优越的预测性能。除了预测组件，Vaxign2 还实现了各种预测后分析，显著增强了用户根据不同疫苗设计原理细化预测结果的能力，并大大减少了用户分析 Vaxign/Vaxign-ML 预测结果的时间。用户提供蛋白质组序列作为输入数据，根据 Vaxign 输出和 Vaxign-ML 得分选择候选物，并进行预测后分析。Vaxign2 还包含了大约 36 种病原体的 398 种蛋白质组中的近 100 万个蛋白质的预先计算结果。作为一个演示，Vaxign2 被有效地用于分析导致 COVID-19 的冠状病毒 SARS-CoV-2。Vaxign2 的综合框架可以支持更好和更合理的疫苗设计。Vaxign2 可在 http://www.violinet.org/vaxign2 上公开访问。
