Comparative genomics of protein structure and function

描述(由申请人提供)：这项工作旨在识别蛋白质功能决定因素，并在蛋白质组中对它们进行比较，以预测蛋白质功能。该方法基于系统基因组学算法，进化踪迹(ET)，用于识别蛋白质中的关键功能残基；以及ET注释(ETA)算法，从ET分析3D模板中提取描述结合或催化所涉及的关键残基的组成和构象，然后在其他结构中搜索与这些3D模板的几何匹配，从而提出共同的功能。初步数据已经通过计算和实验广泛验证了ET，ETA已经成为解释结构基因组蛋白质功能的有用工具。然而，这两种方法仍然可以在敏感性、特异性和可扩展性方面取得进展。为此，我们在目标1中提出，第一，通过优化输入序列的选择和一种新的残基功能重要性的测量，来改进关键功能残基的ET鉴定，第二，改进3D模板的选择。在目标2中，我们提出了一种新的基于网络的注释扩散方法，可以一次比较所有3D模板匹配，并添加来自其他来源的功能信息，例如来自未知结构的蛋白质的信息。AIM 3是实验性的，它将通过突变和对具有直接医学意义的蛋白质的分析来测试我们的预测，这些蛋白质包括一种控制细菌耐药性的蛋白质和另一种疟疾耐药性的标志。从长远来看，这些结果应该有助于将蛋白质工程和药物设计集中在蛋白质功能和治疗上最相关的部分，最广泛地说，将大量且呈指数级增长的原始序列和结构数据与生物功能及其分子基础联系起来。 与公共健康相关：现代生物学擅长产生大量关于我们的基因组成和它们编码的蛋白质结构的基本信息。然而，这些潜在有用的信息大多处于闲置状态，无助于我们对疾病的基本生物学的理解，也无助于新药和治疗方法的开发。原因是仍然很难知道这些新基因到底做了什么，以及它们是如何做到这一点的。这项工作开发了计算方法来回答这两个问题。通过这样做，它应该有助于识别新蛋白质的功能，并帮助将它们与病理过程联系起来。例如，为了测试我们的一些工具和预测，我们将试验性地研究两种具有医学意义的蛋白质，一种是协调细菌耐药性的蛋白质，另一种是标记疟疾耐药性的蛋白质。

DESCRIPTION (provided by applicant): This work aims to identify protein functional determinants and to compare them across the proteome to predict protein function. The approach is predicated on a phylogenomic algorithm, the Evolutionary Trace (ET), that identifies key functional residues in proteins; and on ET Annotation (ETA) algorithms, which extract from ET analysis 3D templates, describing the composition and conformation of key residues involved in binding or in catalysis, and then search in other structures for geometric matches to these 3D templates that suggest a common function. Preliminary data have extensively validated ET, both computationally and through experiments, and ETA has become a useful tool to annotate function on structural genomics proteins. Both methods, however, can still gain in sensitivity, specificity and scalability. To do so we propose in Aim 1, first, to improve the ET identification of key functional residues, by optimizing the selection of the input sequences and by a new measure of residue functional importance, and, second, to refine the selection of 3D templates. In Aim 2, we propose a new network-based annotation diffusion method to compare all 3D template matches at once and to add in functional information from other sources, such as from proteins without known structure. Aim 3 is experimental and it will test our predictions through mutations and assays on proteins of direct medical interest including one that controls drug resistance in bacteria and another that is a marker of drug resistance in malaria. In the long term, these results should help to focus protein engineering and drug design to the most functionally and therapeutically relevant parts of a protein, and, most broadly, link the massive and exponentially growing amounts of raw sequence and structure data to biological function and its molecular basis. PUBLIC HEALTH RELEVANCE: Modern biology excels at producing volumes of basic information on the composition of our genes and on the structure of the proteins that they encode. However, much of this potentially useful information lies fallow and does not contribute to our understanding of the basic biology of disease or to the development of new drugs and treatments. The reason is that it remains difficult to know what these new genes actually do, and how they do it. This work develops computational methods to answer both questions. In so doing it should help identify the function of novel protein and help connect them to pathological processes. For example, to test some of our tools and predictions, we will experimentally study two proteins of medical interest, one that orchestrates drug resistance in bacteria, and another that marks drug resistance in malaria.