Identification of <em>Plasmodium vivax</em> Proteins with Potential Role in Invasion Using Sequence Redundancy Reduction and Profile Hidden Markov Models

BackgroundThis study describes a bioinformatics approach designed to identify Plasmodium vivax proteins potentially involved in reticulocyte invasion. Specifically, different protein training sets were built and tuned based on different biological parameters, such as experimental evidence of secretion and/or involvement in invasion-related processes. A profile-based sequence method supported by hidden Markov models (HMMs) was then used to build classifiers to search for biologically-related proteins. The transcriptional profile of the P. vivax intra-erythrocyte developmental cycle was then screened using these classifiers. ResultsA bioinformatics methodology for identifying potentially secreted P. vivax proteins was designed using sequence redundancy reduction and probabilistic profiles. This methodology led to identifying a set of 45 proteins that are potentially secreted during the P. vivax intra-erythrocyte development cycle and could be involved in cell invasion. Thirteen of the 45 proteins have already been described as vaccine candidates; there is experimental evidence of protein expression for 7 of the 32 remaining ones, while no previous studies of expression, function or immunology have been carried out for the additional 25. ConclusionsThe results support the idea that probabilistic techniques like profile HMMs improve similarity searches. Also, different adjustments such as sequence redundancy reduction using Pisces or Cd-Hit allowed data clustering based on rational reproducible measurements. This kind of approach for selecting proteins with specific functions is highly important for supporting large-scale analyses that could aid in the identification of genes encoding potential new target antigens for vaccine development and drug design. The present study has led to targeting 32 proteins for further testing regarding their ability to induce protective immune responses against P. vivax malaria.研究背景 本研究描述了一种用于鉴定可能参与网织红细胞入侵的间日疟原虫（Plasmodium vivax）蛋白的生物信息学方法。具体而言，研究基于不同的生物学参数构建并优化了多组蛋白质训练集，这些参数包括分泌相关实验证据以及/或参与入侵相关过程的实验证据。随后，依托隐马尔可夫模型（hidden Markov models, HMMs）的基于特征谱的序列分析方法被用于构建分类器，以搜寻具有生物学相关性的蛋白质。随后，利用这些分类器对间日疟原虫红细胞内发育周期的转录谱进行了筛选。 研究结果 本研究设计了一种结合序列冗余度降低与概率特征谱的生物信息学方法，用于鉴定间日疟原虫潜在分泌蛋白。通过该方法，共鉴定得到45种在间日疟原虫红细胞内发育周期中可能分泌、且可能参与细胞入侵过程的蛋白质。在这45种蛋白中，已有13种被报道为疫苗候选靶点；剩余32种蛋白中，有7种具备蛋白质表达的实验证据，而另外25种蛋白此前尚未有关于其表达、功能或免疫学特性的相关研究。 研究结论 本研究结果证实，基于特征谱的隐马尔可夫模型等概率技术可优化序列相似性搜索。此外，诸如使用Pisces或Cd-Hit进行序列冗余度降低等调整手段，可基于合理且可重复的测量指标实现数据聚类。这类针对特定功能蛋白的筛选方法，对于支撑大规模数据分析至关重要，可为识别编码潜在新型疫苗靶点抗原与药物设计相关基因的研究提供助力。本研究最终筛选得到32种蛋白，可用于后续评估其诱导抗间日疟原虫疟疾保护性免疫应答的能力。