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ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates

机译:REVAC:反向疫苗学计算管道,用于原核蛋白质疫苗候选的优先级排序

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BACKGROUND:Reverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.RESULTS:We present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac's orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69?Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.CONCLUSION:ReVac's use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein's features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.
机译:背景:在实验验证之前,反向疫苗学加速了潜在疫苗候选物(PVC)的发现。目前的程序通常使用一种细菌蛋白质来通过使用特征预测程序或机器学习方法通​​过滤波架构来识别PVC。过滤方法可以基于所用预测工具的准确性的限制来消除潜在的抗原。机器学习方法严重依赖于具有实验验证的抗原(阳性对照)和非保护 - 抗原(阴性对照)的训练数据集的选择。使用一种或少量的细菌蛋白质蛋白质不能评估菌株中的PVC保存,疫苗抗原的一个重要特征。结果:我们呈现REVAC,其在不滤除蛋白质的情况下实现特征预测计划的一套胰凝集,并基于预测的候选者进行评分在策划的正和负控制PVCS数据集中制作。 REVAC调查几种评估蛋白质保护的基因组,以及DNA和蛋白质重复,这可能导致PVC的可变表达。 REVAC的保守基因的直核聚类,识别核心和可分配的基因组成分。这对于确定给定病原体的分离物群中PVC的保护程度是有用的。然后基于保护和基于整体特征的评分来优先考虑潜在的疫苗候选者。我们介绍了Revac的应用,应用于69?Moraxella catarrhalis和270个非易形的血液渗流感基因组,分别优先考虑64和29个蛋白质作为PVCs。结论:REVAC使用评分方案的使用为后续的实验测试等级进行PVC。它在使用若干预测工具的特​​征预测中采用基于冗余的方法。蛋白质的特征是整理的,并且每个蛋白质在评分方案上排名。在REVAC中进行的多基因组分析允许从PAN基因组角度综合概述PVC,作为任何细菌亚基疫苗设计的必要性前列。 Revac优先考虑两种人类呼吸道病原体的PVC,鉴定了新颖和先前验证的PVC。

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