Computational Annotation of Orphan Metabolic Activities

描述（由申请人提供）：即使是最先进的同源性方法也不能注释与已知酶没有或有很远序列同一性的代谢基因。这对网络重建提出了显著的障碍，因为大约30%- 40%（>1500）的已知代谢活性仍然是孤儿，即在任何生物体中都没有已知的蛋白质催化这些活性。孤儿活动问题的规模之大，使它可以说是现代生物化学中最大的一个挑战。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利用分配给剩余间隙的网络邻居的基因作为孤儿活动基因分配的约束条件。我们证明了该方法的性能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50%以上的情况下预测正确的基因，而不需要任何序列同源性信息。计算结果表明，在研究较少的生物体中，预测准确度也将保持较高。利用所开发的方法，我们已经鉴定并验证了一个负责E.其代谢活性在超过25年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.）的情况。我们将研究各种机器学习方法和适应度函数整合基于上下文的描述符的能力。根据开发的方法，我们将预测测序生物体中的所有孤儿活性。3.）第三章这些预测将通过一个可搜索的、不断更新的Web服务器提供。我们还将开发一种检测功能性错误注释的方法，并将其应用于所有公共代谢数据库。4.）在与苏黎世联邦理工学院Uwe Sauer博士和哈佛大学乔治Church博士的实验室合作下，我们将对至少50个在大肠杆菌中没有紧密序列同源物的预测基因进行实验性测试。coli，B.枯草芽孢杆菌S.啤酒。

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.