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无法注释与已知酶没有或远程序列相同的代谢基因。这给网络重建带来了重大障碍，因为大约30%- 40% （bbb1500）的已知代谢活动仍然是孤儿，即在任何生物体中都没有已知的催化这些活动的蛋白质。孤儿活性问题的规模使其成为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来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分配孤儿活动基因的约束。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，而不需要任何序列同源性信息。计算表明，在研究较少的生物体中，预测精度也将保持很高。使用开发的方法，我们已经确定并验证了负责大肠杆菌代谢活性的基因，该基因在超过25年的时间里一直处于孤儿状态。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。基于开发的方法，我们将对测序生物体中的所有孤儿活动进行预测。3)。这些预测将通过一个可搜索和不断更新的Web服务器提供。我们还将开发一种检测功能错误注释的方法，并将其应用于所有公共代谢数据库。4)。我们将与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，对大肠杆菌、枯草芽孢杆菌和酿酒杆菌中至少50个预测基因进行实验测试。

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.