Computational Annotation of Orphan Metabolic Activities

描述(由申请人提供)：即使是最先进的同源方法也不能注释与已知酶没有或远程序列相同的代谢基因。这是网络重建的一个重大障碍，因为大约30%-40%(&gt；1500)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。孤儿活动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剩余的空白。该方法的主要思想是使用分配给剩余空隙的网络邻居的基因作为约束来分配孤儿活动的基因。我们证明，这种方法的性能明显优于更简单或现有的方法。我们在模型生物中的交叉验证结果表明，在没有任何序列同源性信息的情况下，所提出的方法可以在50%以上的情况下预测正确的基因。计算表明，在研究较少的生物体中，预测精度也将保持较高水平。使用开发的方法，我们已经识别并验证了一个负责大肠杆菌代谢活动的基因，该基因在超过25年的时间里一直是孤儿。该提案有四个具体目标：1.我们将为大多数被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，我们将对已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并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.)与苏黎世理工学院的Uwe Sauer博士和哈佛大学的George Church博士的实验室合作，我们将在大肠杆菌、枯草杆菌、酿酒酵母中对至少50个没有紧密序列同源的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.