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首页> 美国卫生研究院文献>Frontiers in Genetics >A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen
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A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen

机译：一种基于基因的正选择检测方法以使用弓形虫作为测试案例的原生动物病原鉴定疫苗候选者

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Over the last two decades, various in silico approaches have been developed and refined that attempt to identify protein and/or peptide vaccines candidates from informative signals encoded in protein sequences of a target pathogen. As to date, no signal has been identified that clearly indicates a protein will effectively contribute to a protective immune response in a host. The premise for this study is that proteins under positive selection from the immune system are more likely suitable vaccine candidates than proteins exposed to other selection pressures. Furthermore, our expectation is that protein sequence regions encoding major histocompatibility complexes (MHC) binding peptides will contain consecutive positive selection sites. Using freely available data and bioinformatic tools, we present a high-throughput approach through a pipeline that predicts positive selection sites, protein subcellular locations, and sequence locations of medium to high T-Cell MHC class I binding peptides. Positive selection sites are estimated from a sequence alignment by comparing rates of synonymous (dS) and non-synonymous (dN) substitutions among protein coding sequences of orthologous genes in a phylogeny. The main pipeline output is a list of protein vaccine candidates predicted to be naturally exposed to the immune system and containing sites under positive selection. Candidates are ranked with respect to the number of consecutive sites located on protein sequence regions encoding MHCI-binding peptides. Results are constrained by the reliability of prediction programs and quality of input data. Protein sequences from Toxoplasma gondii ME49 strain (TGME49) were used as a case study. Surface antigen (SAG), dense granules (GRA), microneme (MIC), and rhoptry (ROP) proteins are considered worthy T. gondii candidates. Given 8263 TGME49 protein sequences processed anonymously, the top 10 predicted candidates were all worthy candidates. In particular, the top ten included ROP5 and ROP18, which are T. gondii virulence determinants. The chance of randomly selecting a ROP protein was 0.2% given 8263 sequences. We conclude that the approach described is a valuable addition to other in silico approaches to identify vaccines candidates worthy of laboratory validation and could be adapted for other apicomplexan parasite species (with appropriate data).
机译：在过去的二十年中，已经开发和改进了各种计算机方法，试图从靶病原体的蛋白质序列中编码的信息性信号中识别蛋白质和/或肽疫苗候选物。迄今为止，还没有信号清楚地表明一种蛋白质将有效地促进宿主的保护性免疫应答。这项研究的前提是，在免疫系统中处于积极选择状态的蛋白质比暴露于其他选择压力的蛋白质更可能是合适的候选疫苗。此外，我们的期望是编码主要组织相容性复合物（MHC）结合肽的蛋白质序列区域将包含连续的阳性选择位点。使用可免费获得的数据和生物信息学工具，我们通过一条管道提供了一种高通量方法，该方法可以预测阳性选择位点，蛋白质亚细胞位置以及中高T细胞MHC I类结合肽的序列位置。通过比较系统发育直系同源基因的蛋白质编码序列之间的同义（dS）和非同义（dN）替换率，可以从序列比对估计阳性选择位点。主要的输出是预计将天然暴露于免疫系统并包含阳性选择位点的蛋白质疫苗候选物清单。相对于位于编码MHCI结合肽的蛋白质序列区域上的连续位点的数量对候选者进行排名。结果受到预测程序的可靠性和输入数据质量的限制。以弓形虫ME49株（TGME49）的蛋白质序列为例。表面抗原（SAG），致密颗粒（GRA），微neme（MIC）和rhoptry（ROP）蛋白被认为是刚地弓形虫的候选物。给定8263个TGME49蛋白质序列的匿名处理，排名前10位的候选候选者都是有价值的候选者。特别地，前十名包括ROP5和ROP18，它们是弓形虫的毒力决定因素。给定8263个序列，随机选择ROP蛋白的机会为0.2％。我们得出的结论是，所描述的方法是对其他计算机方法的宝贵补充，可用于鉴定值得实验室验证的候选疫苗，并且可以适用于其他apicomplexan寄生虫物种（具有适当的数据）。

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