GlaxoSmithKline, Siena, Italy.
Dipartimento di Scienze Cliniche e Biologiche, Università degli Studi di Torino, Turin, Italy.
Front Immunol. 2019 Feb 14;10:113. doi: 10.3389/fimmu.2019.00113. eCollection 2019.
Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results.
反向疫苗学(RV)是一种通过计算分析筛选病原体蛋白质组来识别潜在疫苗候选物(PVC)的广泛应用方法。自 20 世纪 90 年代初首次应用于 B 组(MenB)疫苗以来,已经开发了几种实现首个 RV 协议不同变体的软件程序。然而,迄今为止,还没有对这些不同的 RV 工具进行全面审查。我们比较了六个针对细菌疫苗设计的应用程序(NERVE、Vaxign、VaxiJen、Jenner-predict、Bowman-Heinson 和 VacSol),这些程序针对的是 11 种病原体,这些病原体都有经过精心整理的已知细菌保护性抗原(BPA)列表。我们报告了以下结果:(1)用于选择 PVC 的标准和程序的比较;(2)计算运行时间;(3)在确定为 PVC 的蛋白质组分数、在 PVC 中确定的 BPA 分数和富集度方面的性能。该综述表明,没有一个程序能够召回 100%的测试 BPA 集,并且蛋白质的输出列表之间的一致性很差,这表明在优先考虑疫苗候选物的过程中,不要依赖单一的 RV 工具响应。就预测为良好候选物的蛋白质组分数和 BPA 的召回率而言,Bowman(1)提出的机器学习方法(2)增强后的版本表现最佳。尽管它比其他方法表现更出色,但它也存在缺点,即非专家用户难以获得,并且结果与训练数据集组成之间存在很强的依赖性。总之,我们认为,要显著提高下一个 RV 方法的性能,进一步的研究应集中在提高现有蛋白质注释工具的准确性上,并利用应用于生物数据集的机器学习技术的优势,这些数据集还可以通过纳入和整理具有阴性实验结果的细菌蛋白质来扩展。