Computational methods for uncovering protein function in Plasmodium falciparum

描述(申请人提供)：疟疾是最常见的人类传染病之一，估计每年有3亿至5亿病例，每年有100万至300万人死亡。疟疾是由原生动物寄生虫引起的，人类最严重的疾病是由恶性疟原虫引起的。恶性疟原虫基因组已完成全序列测定。值得注意的是，它识别的蛋白质中只有55%具有任何预测或已知的功能注释，而且该有机体的大部分核心机制仍未确定，从而大大阻碍了我们对该有机体和疟疾的了解。由于传统的生物信息学方法在揭示恶性疟原虫蛋白质功能方面取得的成功有限，本研究的长期目标是开发对这一任务更有效的新的计算方法。我们的框架集中在更好地识别蛋白质结构域，即蛋白质的结构、功能和进化单位，并将已发现的恶性疟原虫蛋白结构域与与已知蛋白质功能相关的特征结构域连接起来。我们的方法利用比较基因组学、图论方法和敏感的概率轮廓-轮廓比较，所有这些都在一个强大的计算管道中。这一建议的具体目的是(1)利用密切相关基因组中的同源序列发现恶性疟原虫蛋白序列中的假定结构域，并利用这些结构域识别与已知功能特征蛋白结构域的相似性。(2)利用某些基序和结构域在同一序列中同时出现的趋势，增加具有预测功能基序和结构域的恶性疟原虫蛋白的数量。(3)对一组有代表性的预测进行实验测试，以发现新的恶性疟原虫生物学并评估我们的计算管道。拟议的技术在扩大恶性疟原虫内蛋白质功能注释的数量方面具有巨大的潜力，从而加速了旨在开发针对人类疟疾病原体的抗疟疾药物靶标的正在进行的研究工作。 公共卫生相关性： 疟疾是最常见的人类传染病之一，估计每年有3亿至5亿病例，每年有100万至300万人死亡。人类最严重的疾病是由恶性疟原虫引起的。这项拟议的研究旨在显著扩大/恶性疟原虫/的蛋白质功能注释的数量，以加速我们对人类疟疾病原体的理解。

DESCRIPTION (provided by applicant): Malaria is one of the most common human infectious diseases, with an estimated 300-500 million cases a year and between one and three million yearly deaths. Malaria is caused by protozoan parasites, with the most serious forms of the disease in human caused by Plasmodium falciparum. The P. falciparum genome has been fully sequenced. Remarkably, only 55% of its identified proteins have any predicted or known functional annotations, and much of the organism's core machinery remains unidentified, thereby significantly hampering our understanding of this organism and of malaria. Since traditional bioinformatics approaches have had limited success in uncovering P. falciparum protein functions, the long-term goal of this research is to develop novel computational approaches that are more effective for this task. Our framework is centered on better identification of protein domains, the structural, functional and evolutionary units of proteins, and linking uncovered P. falciparum protein domains to well-characterized domains associated with known protein functions. Our approaches leverage comparative genomics, graph-theoretic methods, and sensitive probabilistic profile-profile comparisons, all within a robust computational pipeline. The specific aims of this proposal are (1) To uncover putative domains within P. falciparum protein sequences using homologous sequences in closely related genomes, and to use these to identify similarity to known functionally characterized protein domains. (2) To increase the number of P. falciparum proteins with predicted functional motifs and domains by exploiting the tendency of certain motifs and domains to occur together within the same sequence. (3) To experimentally test a representative set of predictions, in order to uncover new P. falciparum biology and to evaluate our computational pipeline. The proposed techniques have significant potential for expanding the number of protein functional annotations within P. falciparum, and for therefore accelerating ongoing research efforts aimed at developing anti-malarial drug targets against the causative agent of human malaria. PUBLIC HEALTH RELEVANCE: Malaria is one of the most common human infectious diseases, with an estimated 300- 500 million cases a year and between one and three million yearly deaths. The most serious forms of the disease in human are caused by /P. falciparum/. The proposed research aims to significantly expand the number of protein functional annotations for /P. falciparum/, in order to accelerate our understanding of the causative agent of human malaria.