Structural Genomics of Pathogenic Protozoa

描述(申请人提供)：致病菌的结构基因组学 原生动物(SGPP)联盟旨在实现高通量检测 具有重大全球医学意义的致病原生动物的蛋白质结构。 其中包括几种负责睡眠的锥虫。 疾病、恰加斯病以及皮肤、内脏和粘膜皮肤 利什曼病；恶性疟原虫，致命疟疾的病原体 每年有数亿病例和一两百万人死亡， 主要是孩子。这些生物只与其他物种有远亲关系。 并具有许多独特的生物学特征。我们的目标是确定一个 NN可溶性、异构体和膜蛋白的总量。 拟议研究的特点是： 1.利用蛋白质结构预测方法和靶点的医学相关性 选拔； 2.快速评估数千个基因的表达水平和溶解度 通过来自不同菌株的扰乱序列获得的蛋白质变体； 3.用双杂交方法发现和求解可溶性的结构 杂多聚体； 4.通过双杂交，发现了膜蛋白 与可溶性伴侣蛋白相互作用的方法和FV噬菌体文库 用于共结晶和MAD定相； 5.机器人技术在结晶、晶体安装、数据等方面的广泛应用 采集步骤； 6.含溴共结晶库的开发； 7.探索晶体退火在改善马赛克和 决议； 8.在电子密度解释中使用预测的蛋白质结构； 9.使用配基对接程序、深序列家族比对和非常 从结构导出函数的弱结构同调； 10.创建SGPP网站和SGPP关系数据库，通过这些协议 用于蛋白质的表达、纯化、结晶和结构 确定，以及坐标和结构系数是可用的 对公众开放。

DESCRIPTION (provided by applicant): The Structural Genomics of Pathogenic Protozoa (SGPP) consortium aims to achieve high-throughput determination of protein structures from pathogenic protozoa of major global medical relevance. These include several species of Trypanosomatids, responsible for sleeping sickness, Chagas' disease, and cutaneous, visceral and mucocutaneous leishmaniasis; and Plasmodium falciparum, the causative agent of lethal malaria with several hundred million cases annually and one to two million deaths, mainly children. These organisms are only distantly related to other species and characterized by many unique biological features. We aim to determine a total of NN soluble, heteromultimeric and membrane proteins. Special characteristics of the proposed research are: 1. Using protein structure prediction methods and medical relevance in target selection; 2. Rapid evaluation of expression levels and solubility of thousands of variants of proteins obtained by scrambled sequences from different strains; 3. Using two-hybrid methods to discover and solve structures of soluble heteromultimers; 4. Significant emphasis on membrane proteins with discovery, by two-hybrid methods and Fv phage libraria,of interacting soluble partner proteins to be used for co-crystallization and MAD phasing; 5. Intensive use of robotics in the crystallization, crystal mounting, data collection steps; 6. Development of bromine-containing co-crystallant libraries; 7. Exploring the power of crystal annealing in improving mosaicity and resolution; 8. Using predicted protein structures in electron density interpretation; 9. Using ligand docking procedures, deep sequence family alignments and very weak structural homologies to derive function from structure; 10. Create a SGPP website and a SGPP relational data base by which protocols for protein expression, purification, crystallization and structure determination, as well as coordinates and structure factors are made available to the public.