Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.
BMC Bioinformatics. 2013 Jul 1;14:211. doi: 10.1186/1471-2105-14-211.
Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein's adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs.
A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset.
Better prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html.
基于重组蛋白的亚单位疫苗已在预防传染病方面取得了显著成效，有望满足未来疫苗开发的需求。计算方法，特别是反向疫苗学（RV）方法，在从蛋白质组中鉴定蛋白质疫苗候选物（PVC）方面具有巨大的潜力。现有的保护性抗原预测软件和网络服务器的预测准确性较低，导致在疫苗开发中的应用受到限制。除了机器学习技术外，这些软件和网络服务器仅将蛋白质的黏附可能性作为鉴定 PVC 的标准。众所周知，参与宿主-病原体相互作用和发病机制的几种非黏附功能类别的蛋白质可提供针对细菌感染的保护。因此，对细菌发病机制的了解有可能鉴定出 PVC。
开发了一个名为 Jenner-Predict 的网络服务器，用于从细菌病原体的蛋白质组中预测 PVC。该网络服务器通过考虑来自黏附蛋白、毒力蛋白、入侵蛋白、孔蛋白、鞭毛蛋白、定植蛋白、毒素蛋白、胆碱结合蛋白、青霉素结合蛋白、转位结合蛋白、纤维蛋白结合蛋白和溶质结合蛋白等蛋白类别的已知功能域，针对宿主-病原体相互作用和发病机制。它预测含有上述结构域的非细胞溶质蛋白为 PVC。它还通过比较与实验已知的 IEDB 表位、不存在自身免疫和不同菌株中的保守性，来预测 PVC 的疫苗潜力。预测的 PVC 进行了优先级排序，以便只有少数有前途的 PVC 可以通过实验验证。该网络服务器的性能是针对 Protegen 数据库中报告的不同细菌类别的已知保护性抗原以及用于 VaxiJen 服务器开发的数据集进行评估的。该网络服务器能够有效地预测来自肺炎链球菌和大肠杆菌蛋白质组的已知疫苗候选物。Jenner-Predict 服务器的性能优于 NERVE、Vaxign 和 VaxiJen 方法。对于 Protegen 数据集，它的灵敏度分别为 0.774 和 0.711，而对于后者数据集，特异性为 0.940。
Jenner-Predict 网络服务器更高的预测准确性表明，参与宿主-病原体相互作用和发病机制的结构域是预测 PVC 的更好标准。该网络服务器成功预测了属于不同功能类别的最大已知 PVC。Jenner-Predict 服务器可免费访问 http://117.211.115.67/vaccine/home.html。
