Computational Annotation of Orphan Metabolic Activities

孤儿代谢活动的计算注释

• 批准号: 7653790
• 负责人: Dennis Vitkup
• 金额: $ 39.73万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-14 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7653790
• 关键词: Animal Model; Arts; Base Sequence; Biochemical; Biochemical Pathway; Biochemistry; Biological Neural Networks; Church; Collaborations; Communities; Databases; Decision Trees; Descriptor; Enzymes; Escherichia coli; Evolution; Gene Fusion; Genes; Genetic; Internet; Laboratories; Link; Machine Learning; Metabolic; Methodology; Methods; Operon; Organism; Orphan; Pathway interactions; Performance; Positioning Attribute; Proteins; Research Personnel; Saccharomyces cerevisiae; Sequence Homologs; Sequence Homology; Specific qualifier value; Structure; Testing; Update; Validation; base; computer based statistical methods; fitness; gene correction; gene function; genome sequencing; metabolomics; protein function; reconstruction

DESCRIPTION (provided by applicant): Even state-of-the-art homology methods cannot annotate metabolic genes with no or remote sequence identity to known enzymes. This presents a significant obstacle to network reconstruction, as about 30%- 40% (>1500) of known metabolic activities remain orphan, i.e. there are no known proteins catalyzing these activities in any organism. The scale of the orphan activities problem makes it arguably the single biggest challenge of modern biochemistry. We propose to develop, experimentally validate, and make available to the scientific community an efficient computational approach to fill the remaining gaps in metabolic networks. The main idea of the proposed method is to use genes assigned to the network neighbors of the remaining gaps as constraints in assigning genes for orphan activities. We demonstrate that this approach significantly outperforms simpler or existing methods. Our cross-validated results in model organisms demonstrate that the proposed method can predict the correct genes in more than 50% of the cases, without any sequence homology information. The calculations indicate that the prediction accuracy will also remain high in less studied organisms. Using the developed method we have already identified and validated a gene responsible for an E. coli metabolic activity which remained orphan for more than 25 years. There are four specific aims of the proposal: 1.) We will calculate the appropriate context-based descriptors of protein function for the majority of sequenced organisms. Many new functional descriptors will be developed and used for the predictions. 2.) We will investigate the ability of various machine learning approaches and fitness functions to integrate context-based descriptors. Based on the developed methodology we will make predictions for all orphan activities in sequenced organisms. 3.) The predictions will be available through a searchable and constantly updated Web server. We will also develop a method to detect functional misannotations and apply it to all public metabolic databases. 4.) In collaboration with the laboratories of Dr. Uwe Sauer (ETH Zurich) and Dr. George Church (Harvard) we will experimentally test at least 50 of the predicted genes without close sequence homologs in E. coli, B. subtilis, S. cerevisiae.

描述（由申请人提供）：即使是最先进的同源方法也无法注释与已知酶没有或远程序列同一性的代谢基因。这给网络重建带来了重大障碍，因为大约 30% - 40% (>1500) 的已知代谢活动仍然是孤立的，即在任何生物体中都没有已知的蛋白质催化这些活动。孤儿活动问题的规模使其可以说是现代生物化学面临的最大挑战。我们建议开发、实验验证并向科学界提供一种有效的计算方法，以填补代谢网络中的剩余空白。该方法的主要思想是使用分配给剩余间隙的网络邻居的基因作为为孤儿活动分配基因的约束。我们证明这种方法明显优于更简单或现有的方法。我们在模式生物中的交叉验证结果表明，所提出的方法可以在超过 50% 的情况下预测正确的基因，而无需任何序列同源性信息。计算表明，在研究较少的生物体中，预测准确性也将保持较高水平。使用开发的方法，我们已经鉴定并验证了负责大肠杆菌代谢活动的基因，该基因在 25 年多的时间里一直是孤儿。该提案有四个具体目标： 1.) 我们将为大多数已测序的生物体计算适当的基于上下文的蛋白质功能描述符。许多新的功能描述符将被开发并用于预测。 2.) 我们将研究各种机器学习方法和适应度函数集成基于上下文的描述符的能力。根据开发的方法，我们将对测序生物体中的所有孤儿活动进行预测。 3.) 预测将通过可搜索且不断更新的网络服务器提供。我们还将开发一种方法来检测功能错误注释并将其应用于所有公共代谢数据库。 4.) 与 Uwe Sauer 博士（苏黎世联邦理工学院）和 George Church 博士（哈佛大学）的实验室合作，我们将在大肠杆菌、枯草芽孢杆菌、枯草芽孢杆菌中对至少 50 个没有密切序列同源物的预测基因进行实验测试。 . 酿酒酵母。