ACTIVE SITE SIGNATURES FOR AUTOMATIC UPDATES OF SFLD SUPERFAMILIES

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 使用计算预测的结构-功能连接的主要未解决的问题是，虽然我们可以基于许多类型的相似性度量以良好的统计显著性准确地聚类蛋白质序列和结构，但是这些聚类如何连接到功能类尚不清楚。虽然简单的方法，如直向同源物预测可以实现很好的结果，序列是非常相似的，或包含容易识别的基序，区分功能类，对于许多蛋白质超家族成功的预测是远远不够的。这是SFLD中功能多样的超家族的情况。这些是同源的酶组，它们使用不同的底物进行不同的化学转化，但都具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-功能映射。 (For关于机械上不同的酶超家族的更多信息，参见Gerlt & Babelton，Annual Rev Biochem.2001，pp. 209-46.） 由于给定超家族中的不同功能家族看起来相似，但执行不同的特异性反应，因此它们难以注释且容易错误注释，在Genbank NR和TrEMBL档案数据库中显示错误注释水平高达80%（Schnoes，Dodevski和Babelmann，提交）。由于序列信息仍然是未来在大容量，需要自动化的方法来更新SFLD超家族与新确定的序列，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功能分配的方法。开发一种实现这一目标的方法一直是Babeland和Ferrin小组与维克森林大学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.