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用于设计针对主要病原菌细菌亚单位疫苗的网络资源。

A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria.

机构信息

Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.

Centre for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.

出版信息

Front Immunol. 2018 Oct 2;9:2280. doi: 10.3389/fimmu.2018.02280. eCollection 2018.

Abstract

Evolution has led to the expansion of survival strategies in pathogens including bacteria and emergence of drug resistant strains proved to be a major global threat. Vaccination is a promising strategy to protect human population. Reverse vaccinology is a more robust vaccine development approach especially with the availability of large-scale sequencing data and rapidly dropping cost of the techniques for acquiring such data from various organisms. The present study implements an immunoinformatic approach for screening the possible antigenic proteins among various pathogenic bacteria to systemically arrive at epitope-based vaccine candidates against 14 pathogenic bacteria. Thousand four hundred and fifty nine virulence factors and Five hundred and forty six products of essential genes were appraised as target proteins to predict potential epitopes with potential to stimulate different arms of the immune system. To address the self-tolerance, self-epitopes were identified by mapping on 1000 human proteome and were removed. Our analysis revealed that 21proteins from 5 bacterial species were found as virulent as well as essential to their survival, proved to be most suitable vaccine target against these species. In addition to the prediction of MHC-II binders, B cell and T cell epitopes as well as adjuvants individually from proteins of all 14 bacterial species, a stringent criteria lead us to identify 252 unique epitopes, which are predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants. In order to provide service to scientific community, we developed a web server VacTarBac for designing of vaccines against above species of bacteria. This platform integrates a number of tools that includes visualization tools to present antigenicity/epitopes density on an antigenic sequence. These tools will help users to identify most promiscuous vaccine candidates in a pathogenic antigen. This server VacTarBac is available from URL (http://webs.iiitd.edu.in/raghava/vactarbac/).

摘要

进化导致了病原体(包括细菌)生存策略的扩展,而耐药菌株的出现已被证明是一个主要的全球威胁。疫苗接种是保护人类人口的一种有前途的策略。反向疫苗学是一种更强大的疫苗开发方法,特别是随着大规模测序数据的可用性以及从各种生物体获取此类数据的技术成本迅速下降。本研究实施了一种免疫信息学方法,从各种致病菌中筛选可能的抗原蛋白,系统地获得针对 14 种致病菌的基于表位的疫苗候选物。评估了 1459 种毒力因子和 546 种必需基因产物作为靶蛋白,以预测具有刺激免疫系统不同分支潜力的潜在表位。为了解决自身耐受问题,通过对 1000 个人类蛋白质组进行映射来识别自身表位并将其去除。我们的分析表明,从 5 种细菌的 21 种蛋白质中发现了既具有毒力又对其生存至关重要的蛋白质,被证明是针对这些物种最适合的疫苗靶标。除了从所有 14 种细菌的蛋白质中单独预测 MHC-II 结合物、B 细胞和 T 细胞表位以及佐剂外,严格的标准还使我们能够识别 252 个独特的表位,这些表位被预测为 T 细胞表位、B 细胞表位、MHC II 结合物和疫苗佐剂。为了向科学界提供服务,我们开发了一个名为 VacTarBac 的网络服务器,用于设计针对上述细菌物种的疫苗。该平台集成了许多工具,包括可视化工具,用于在抗原序列上呈现抗原性/表位密度。这些工具将帮助用户在一种病原体抗原中识别最混杂的疫苗候选物。该服务器 VacTarBac 可从 URL(http://webs.iiitd.edu.in/raghava/vactarbac/)获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da64/6190870/5c527a22f62b/fimmu-09-02280-g0001.jpg

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