Computational methods for uncovering protein function in Plasmodium falciparum

揭示恶性疟原虫蛋白质功能的计算方法

基本信息

批准号：
7773079
负责人：
MONA SINGH
金额：
$ 23.91万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-03-01 至 2012-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7773079
关键词：
6H,8H-3,4-dihydropyrimido(4,5-c)(1,2)oxazin-7-one Amino Acid Sequence Amino Acids Antimalarials Bioinformatics Biology Blast Cell Cessation of life Communicable Diseases Computer software Computing Methodologies Databases Disease Drug Delivery Systems Genome Goals Grant Graph Human Individual Knowledge Libraries Malaria Methodology Methods Organism Parasites Peptide Sequence Determination Phylogenetic Analysis Plasmodium falciparum Protein Structure Initiative Proteins Proteome Research SH3 Domains Sequence Homologs Signal Transduction Signaling Protein Techniques Tertiary Protein Structure Testing Time Trefoil Motif Zinc Fingers base comparative genomics computer framework genome sequencing improved knowledge base link protein novel numb protein protein function public health relevance statistics success transcription factor

项目摘要

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.

描述（由申请人提供）：疟疾是最常见的人类传染病之一，每年估计有300-5亿例，每年一到300万例死亡。疟疾是由原生动物寄生虫引起的，该疾病是由恶性疟原虫引起的人类疾病的最严重形式。恶性疟原虫基因组已完全测序。值得注意的是，只有55％的鉴定蛋白具有任何预测或已知的功能注释，并且该生物体的许多核心机械仍然不明显，从而极大地阻碍了我们对这种生物体和疟疾的理解。由于传统的生物信息学方法在发现恶性疟原虫蛋白功能方面取得了有限的成功，因此这项研究的长期目标是开发对这项任务更有效的新型计算方法。我们的框架以更好地识别蛋白质结构域，蛋白质的结构，功能和进化单元，以及将发现的恶性疟原虫蛋白质结构域与与已知蛋白质功能相关的特征良好的结构域联系起来。我们的方法利用了比较基因组学，图理论方法和灵敏的概率概况概况 - 概要性比较，均在强大的计算管道内。该建议的具体目的是（1）使用紧密相关的基因组中的同源序列在恶性疟原虫蛋白质序列中发现推定的结构域，并使用这些序列来识别与已知功能性特征蛋白质结构域的相似性。（2）通过利用某些基序和结构域的趋势在同一序列中一起出现，以增加具有预测功能基序和域的恶性假单胞菌蛋白的数量。（3）在实验测试一组代表性的预测集中，以发现新的恶性疟原虫生物学并评估我们的计算管道。所提出的技术具有扩大恶性疟原虫内蛋白质功能注释数量的巨大潜力，因此为了加速旨在开发针对人类疟疾致病剂的抗癌症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300亿例，每年一到300万例死亡。人类中最严重的疾病形式是由 /p引起的。恶意/拟议的研究旨在显着扩大 /p的蛋白质功能注释的数量。恶性，为了加速我们对人类疟疾的致病药物的理解。