Protein function prediction by statistical phylogenomics

通过统计系统基因组学预测蛋白质功能

• 批准号: 7408105
• 负责人: Steven E Brenner
• 金额: $ 28.88万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7408105
• 关键词: Accounting; Algorithms; Animal Model; Architecture; Biological; Biological Assay; Code; Collaborations; Communities; Complex; Computing Methodologies; Data; Databases; Dependency; Development; Disease; Effectiveness; Family; Family Study; Feedback; Foundations; Future; Genetic; Genome; Health; Human; Learning; Literature; Methods; Modeling; Molecular; Molecular Evolution; Numbers; Pattern; Performance; Phylogenetic Analysis; Protein Databases; Protein Family; Protein Sequence Analysis; Proteins; Public Health; Range; Research; Research Personnel; Statistical Methods; Structure; Surveys; System; Tertiary Protein Structure; Testing; Trees; Validation; Variant; Work; base; blind; genome sequencing; improved; member; nudix hydrolase; pathogen; practical application; programs; protein function; prototype; structural genomics; success

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.

描述（由申请人提供）：基因组和元基因组项目揭示了数百万蛋白的遗传序列，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整合对帐系统发育树中所有可用的功能数据进化关系。这种称为系统基因组学的方法已被宣告为高度准确且概念上的优雅，但是它的应用受到了域专家对艰苦分析的精致依赖的限制。我们将使用系统基因原理来增强，评估和应用统计方法来预测蛋白质功能。我们的方法，称为SIFTER（通过进化关系的功能统计推断）目前作为原型存在。在此提案中，我们将增强核心算法以考虑领域架构，在其方法上更加稳定，并适应蛋白质的可能功能范围更大。我们将改善分子进化模型的关键内部参数，并提高结果的解释性。我们将使该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环境中纳入其他功能预测方法。 Sifter的性能将使用经过充分研究的家庭进行严格评估。我们将与主要蛋白质数据库合作，以中等规模的蛋白质注释应用程序。实验验证对于真正测试Sifter的性能至关重要，并且巧合的是，我们对几种蛋白质家族的生物学理解。我们将使用SIFTER对NUDIX蛋白进行最佳选择进行实验表征。除了分析这些蛋白质外，我们还将对蛋白质的分子功能进行盲目预测，其特征是结构基因组学中心，然后我们将在生物化学上表征提供给我们的有希望的候选蛋白。完整的SIFTER系统应比当前的蛋白质功能预测方法有了显着改进，几乎与所有分子生物学家直接相关。通过解锁基因组序列编码的蛋白质功能信息，这项工作对公共卫生的重要性是明确而直接的。这些方法将允许理解与疾病有关的蛋白质，对于人类和模型生物，对健康和健康所必需的蛋白质。 Sifter的应用还将允许对病原体和共生微生物群的蛋白质的详细了解。这些方法将是对通过基因组项目鉴定的任何蛋白质进行进一步研究的基础。