NERVE 2.0:通过人工智能和用户友好的网络界面促进新的增强的反向疫苗学环境。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-12-18 DOI:10.1186/s12859-024-06004-0
Andrea Conte, Nicola Gulmini, Francesco Costa, Matteo Cartura, Felix Bröhl, Francesco Patanè, Francesco Filippini
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引用次数: 0

摘要

背景:本世纪的疫苗发展始于对B型脑膜炎奈瑟菌的里程碑式研究,报告了反向疫苗学(Reverse Vaccinology, RV)的发明,该技术允许通过计算分析筛选细菌病原体基因组或蛋白质组来确定候选疫苗(VCs)。当第一个集成了风险投资选择工具的风险投资软件NERVE (New Enhanced RV Environment)发布时,它推动了该领域的进一步发展。然而,大多数(如果不是全部的话)RV程序的解决问题的潜力仍然在很大程度上未被实验性疫苗学家利用,这些疫苗学家受到某种程度上困难的接口的损害,需要生物信息学技能。结果:我们在这里报告了NERVE 2.0的开发和发布(可在:https://nerve-bio.org),它保留了NERVE原有的集成和模块化方法,同时显示出比以前版本和其他web-RV程序(Vaxign和Vaxijen)更高的预测性能。我们更新了它的一些模块,并增加了创新的模块,如Loop-Razor,以恢复有希望的候选疫苗的片段或表位预测,用于表位预测结合亲和力和群体覆盖率。以及两个新建立的基于AI(人工智能)的模型:ESPAAN和Virulent。为了提高用户的友好性,NERVE被转移到一个辅导的,基于web的界面,与nosql数据库同意用户随时提交,获取和检索分析结果。结论:经过重新设计和更新的环境,NERVE 2.0允许为所有不同类型的用户定制和改进细菌蛋白疫苗分析。
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NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface.

Background: Vaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills.

Results: We report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org ) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop-Razor, to recover fragments of promising vaccine candidates or Epitope Prediction for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment.

Conclusions: With its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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