Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, 226028, India.
Department of Zoology, University of Lucknow, Lucknow, 226007, India.
BMC Bioinformatics. 2019 Feb 4;19(Suppl 13):468. doi: 10.1186/s12859-018-2482-x.
In the current scenario, designing of world-wide effective malaria vaccine against Plasmodium falciparum remain challenging despite the significant progress has been made in last few decades. Conventional vaccinology (isolate, inactivate and inject) approaches are time consuming, laborious and expensive; therefore, the use of computational vaccinology tools are imperative, which can facilitate the design of new and promising vaccine candidates.
In current investigation, initially 5548 proteins of P. falciparum genome were carefully chosen for the incidence of signal peptide/ anchor using SignalP4.0 tool that resulted into 640 surface linked proteins (SLP). Out of these SLP, only 17 were predicted to contain GPI-anchors using PredGPI tool in which further 5 proteins were considered as malarial antigenic adhesins by MAAP and VaxiJen programs, respectively. In the subsequent step, T cell epitopes of 5 genome derived predicted antigenic adhesins (GDPAA) and 5 randomly selected known malarial adhesins (RSKMA) were analysed employing MHC class I and II tools of IEDB analysis resource. Finally, VaxiJen scored T cell epitopes from each antigen were considered for prediction of population coverage (PPC) analysis in the world-wide population including malaria endemic regions. The validation of the present in silico strategy was carried out by comparing the PPC of combined (MHC class I and II) predicted epitope ensemble among GDPAA (99.97%), RSKMA (99.90%) and experimentally known epitopes (EKE) of P. falciparum (97.72%) pertaining to world-wide human population.
The present study systematically screened 5 potential protective antigens from P. falciparum genome using bioinformatics tools. Interestingly, these GDPAA, RSKMA and EKE of P. falciparum epitope ensembles forecasted to contain highly promiscuous T cell epitopes, which are potentially effective for most of the world-wide human population with malaria endemic regions. Therefore, these epitope ensembles could be considered in near future for novel and significantly effective vaccine candidate against malaria.
尽管在过去几十年中取得了重大进展,但针对恶性疟原虫的全球有效疟疾疫苗的设计仍然具有挑战性。传统疫苗学(分离、灭活和注射)方法既耗时、费力又昂贵;因此,使用计算疫苗学工具势在必行,这可以促进新的有前途的疫苗候选物的设计。
在当前的研究中,首先使用 SignalP4.0 工具仔细选择恶性疟原虫基因组的 5548 种蛋白质,以确定信号肽/锚的发生率,结果得到 640 种表面连接蛋白(SLP)。在这些 SLP 中,只有 17 种被预测含有 PredGPI 工具中的 GPI 锚,其中进一步的 5 种蛋白质分别被 MAAP 和 VaxiJen 程序认为是疟原虫抗原粘附素。在接下来的步骤中,使用 IEDB 分析资源的 MHC 类 I 和 II 工具分析了 5 种基因组衍生的预测抗原粘附素(GDPAA)和 5 种随机选择的已知疟原虫粘附素(RSKMA)的 T 细胞表位。最后,VaxiJen 对每个抗原的 T 细胞表位进行评分,并考虑在包括疟疾流行地区在内的全球人口中进行人群覆盖率(PPC)分析。通过比较 GDPAA(99.97%)、RSKMA(99.90%)和恶性疟原虫实验已知表位(EKE)(97.72%)之间组合(MHC 类 I 和 II)预测表位的 PPC,验证了本研究中的生物信息学策略。
本研究系统地使用生物信息学工具从恶性疟原虫基因组中筛选了 5 种潜在的保护性抗原。有趣的是,这些 GDPAA、RSKMA 和恶性疟原虫表位的 EKE 预测包含高度混杂的 T 细胞表位,这些表位对大多数有疟疾流行地区的全球人口可能是有效的。因此,这些表位可以在不久的将来考虑用于针对疟疾的新型和有效的疫苗候选物。