ACTIVE SITE SIGNATURES FOR AUTOMATIC UPDATES OF SFLD SUPERFAMILIES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 使用计算预测进行结构-功能关联的主要未解决问题是，尽管我们可以基于许多类型的相似性度量准确地对具有良好统计意义的蛋白质序列和结构进行聚类，但这些聚类如何与功能类关联尚不清楚。虽然简单的方法，如直系同源预测，可以取得很好的结果，对于密切相似的序列或包含容易识别的基序区分功能类，对许多蛋白质超家族成功的预测远非微不足道。这就是SFLD中功能多样化的超级家庭的情况。这些是同源的几组酶，使用不同的底物进行不同的化学转化，但都有特定的化学功能或部分反应。SFLD的主要目的是帮助研究人员管理这些类型的超家族，帮助识别这些超家族的新成员，并为这些酶提供明确的结构-功能图谱。(有关机械多样化的酶超家族的更多信息，请参见Gerlt&Babbitt，年度Rev Biochem。2001年，第209-46页。)由于给定超家族中的不同功能家族看起来相似，但执行不同的特定反应，它们很难注释，很容易被错误注释，在Genbank NR和tembl(Schnoes，Dodevski，和Babbitt提交)的档案数据库中，错误注释的水平高达80%。由于序列信息仍然大量可用，因此需要自动化的方法来用新确定的序列更新SFLD超家族，并将它们分配到适当的功能家族。显然，迫切需要改进实现这些职能分配的方法。开发一种实现这一目标的方法一直是Babbitt和Ferrin小组与维克森林大学的Jacquelyn Fetrow教授小组合作的主要重点。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. major unsolved problem for structure-function linkage using computational prediction is that while we can accurately cluster protein sequences and structures with good statistical significance based on many types of similarity metrics, how those clusters link to functional classes is not clear. Although simple approaches such as ortholog prediction can achieve good results for sequences that are closely similar or that contain readily identifiable motifs that distinguish functional classes, for many protein superfamilies successful prediction is far from trivial. This is the case for the functionally diverse superfamilies in the SFLD. These are homologous sets of enzymes that carry out different chemical transformations, using different substrates, but all share a specific chemical functionality or partial reaction. The main purpose of the SFLD is to aid researchers in the curation of these types of superfamilies, to help in the identification of new members of these superfamilies, and to provide an explicit structure-function mapping for these enzymes. (For more information about mechanistically diverse enzyme superfamilies, see Gerlt & Babbitt, Annual Rev Biochem. 2001, pp. 209-46.) Because the different functional families in a given superfamily look similar but perform different specific reactions, they are difficult to annotate and easy to misannotate, showing levels of misannotation as high as 80% in the archival databases Genbank NR and TrEMBL (Schnoes, Dodevski, and Babbitt, submitted). Because sequence information is still coming available in large volumes, automated methods are required to update the SFLD superfamilies with newly determined sequences and assign them to the appropriate functional families. Clearly, improved methods for achieving these functional assignments are urgently needed. Development of an approach to achieve this has been a major focus of the Babbitt and Ferrin groups in collaboration with the group of Prof. Jacquelyn Fetrow of Wake Forest University.