Protein function prediction by statistical phylogenomics

描述（申请人提供）：基因组和宏基因组项目已经揭示了数百万种蛋白质的基因序列，其生物学解释需要了解其功能。预测蛋白质功能的最成功的方法之一是将所有可用的功能数据进化关系整合到一个协调的系统发育树中。这种方法被称为基因组学，被誉为高度准确和概念优雅，但它的应用受到限制，因为它依赖于领域专家的艰苦分析。我们将加强，评估，并应用统计方法预测蛋白质功能，利用基因组学原理。我们的方法，被称为SIFTER（统计推断功能通过进化关系）目前存在的原型。在这个提议中，我们将增强核心算法，以考虑域结构，在其方法中变得更加一致的统计，并适应更大范围的蛋白质可能的功能。我们将改进分子进化模型的关键内部参数，并提高结果的可解释性。我们将使程序能够接受更典型的蛋白质序列进行分析，并使用更广泛的信息（包括数据库注释，序列和结构基序）作为功能的证据。最终，SIFTER将能够在其系统发育背景下结合其他功能预测方法。将使用经过充分研究的系列严格评估SIFTER的性能。我们将与主要的蛋白质数据库合作，部署SIFTER用于蛋白质注释的中等规模应用。实验验证将是必不可少的，真正测试SIFTER的性能，巧合的是，丰富了我们对几个蛋白质家族的生物学理解。我们将使用SIFTER进行最佳选择的Nutrition蛋白的实验表征。除了分析这些蛋白质，我们还将对结构基因组学中心表征的蛋白质的分子功能进行盲预测，然后我们将对提供给我们的有希望的候选蛋白质进行生物化学表征。完成的SIFTER系统应提供一个显着的改进，目前的方法，蛋白质功能预测，直接相关的几乎所有的分子生物学家。这项工作对公共卫生的意义是明确和直接的，通过解锁基因组序列中编码的蛋白质功能信息。这些方法将使人们能够了解人类和模式生物中与疾病有关的蛋白质和健康所必需的蛋白质。SIFTER的应用还将允许详细了解病原体和肠道微生物群的蛋白质。这些方法将为进一步研究通过基因组计划鉴定的任何蛋白质奠定基础。

DESCRIPTION (provided by applicant): Genome and metagenome projects have revealed the genetic sequence of millions of proteins, whose biological interpretation requires understanding of their function. One of the most successful approaches for predicting proteins' functions is the integration of all available functional data evolutionary relationships in a reconciled phylogenetic tree. This method, known as phylogenomics, has been heralded as highly accurate and conceptually elegant, but its application has been limited by its exquisite dependency upon painstaking analyses by domain experts. We will enhance, assess, and apply a statistical method for predicting protein function using phylogenomic principles. Our approach, known as SIFTER (Statistical Inference of Function Through Evolutionary Relationships) presently exists as a prototype. In this proposal, we will enhance the core algorithms to take account of domain architecture, to become more consistently statistical in its approach, and to accommodate a larger range of possible functions for proteins. We will improve the key internal parameters of the molecular evolution model, and improve interpretability of the results. We will make the program capable of accepting more typical protein sequences for analysis, and of using a wider range of information (including database annotations, sequence & structure motifs) as evidence of function. Ultimately, SIFTER will be capable of incorporating other function prediction approaches within its phylogenetic context. The performance of SIFTER will be rigorously assessed using well-studied families. We will collaborate with major protein databases to deploy SIFTER for medium-scale application in protein annotation. Experimental validation will be essential to truly test SIFTER'S performance and, coincidentally, enrich our biological understanding of several protein families. We will use SIFTER to make an optimal selection of Nudix proteins for experimental characterization. In addition to assaying these proteins, we will also make blind predictions of molecular function of proteins being characterized by structural genomics centers, and we will then biochemically characterize promising candidate proteins provided to us. The completed SIFTER system should provide a significant improvement over current approaches for protein function prediction, of direct relevance to nearly all molecular biologists. The significance of this work for public health is clear and immediate, by unlocking protein function information encoded in genome sequences. These methods will allow understanding of proteins implicated in disease and necessary for health, in humans as well as model organisms. Application of SIFTER will also permit detailed understanding of pathogens' and commensal microbiota's proteins. These methods will be a foundation for the further study of any protein identified through genome projects.