ACTIVE SITE SIGNATURES FOR SFLD: ENOLASE SUPERFAMILY

SFLD 的活性位点特征：烯醇酶超家族

• 批准号: 8363627
• 负责人: PATRICIA CLEMENT BABBITT
• 金额: $ 1.68万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8363627
• 关键词: Active Sites; Amino Acid Sequence; Carbon; Chemicals; Chemistry; Collaborations; Data; Databases; Development; Elements; Environment; Enzymes; Family; Funding; Genbank; Genetic Programming; Grant; Imagery; Informatics; Link; Maps; Methods; Metric; National Center for Research Resources; Orthologous Gene; Pattern; Peptide Sequence Determination; Phosphotransferases; Principal Investigator; Proteins; Reaction; Research; Research Infrastructure; Research Personnel; Resources; Site; Source; Structure; System; Technology; Testing; United States National Institutes of Health; Universities; Update; base; biocomputing; cost; enolase; forest; improved; member; protein structure; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. A 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. 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. 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 RBVI in collaboration with the group of Prof. Jacquelyn Fetrow of Wake Forest University. The active site profiling methods developed by Dr. Fetrow have now been integrated with an approach developed in the Babbitt lab, Genetic Algorithm Search for Patterns in Structures: GASPS, to automatically determine 3D templates capable of distinguishing new superfamily members for the purpose of automatically assigning sequences to the specific functional families to which they belong. GASPS will be combined with Fetrow's methods to create sequence and structural motifs for automated clustering of SFLD data. The core elements of the method include a motif-generating technology called "Fuzzy Functional Forms", (FFF), implemented by the tool Protein Active Site Structure Search (PASSS), and the Deacon Active Site Profiler (DASP) which uses three-dimensional, or structure-based, active-site profiling to identify residues located in the spatial environment around the active site. PASSS uses the FFF technology, describing a proteins functional site by the distances between the alpha carbons of three key residues important to the functional site chemistry and the alpha carbons of adjacent residues. Based on the premise that functionally related proteins should have structural similarity at the functional site, PASSS returns related proteins to the starting known functional site. DASP expands on this, extracting the residues that are found in the vicinity of the key residues for each protein, creating motifs from these fragments, and using these fragments to search all sequences in a database to return proteins that may share this function. Use of these tools together, and in an iterative fashion, provides a quick method to putatively functionally characterize both structures and sequences. Preliminary results from this project show exceptional accuracy in distinguishing functionally diverse families in the enolase and the kinase superfamily. The former is one of the annotated superfamilies in the SFLD that serves as a challenging test system for this type of automated effort.

该副本是利用资源的众多研究子项目之一 由NIH/NCRR资助的中心赠款提供。对该子弹的主要支持 而且，副投影的主要研究员可能是其他来源提供的 包括其他NIH来源。 列出的总费用可能 代表subproject使用的中心基础架构的估计量， NCRR赠款不直接向子弹或副本人员提供的直接资金。 使用 计算预测是，尽管我们可以准确地聚类蛋白质 基于许多的统计显着性的序列和结构 相似性指标类型，这些群集如何链接到功能类别 不清楚。尽管诸如直系同源预测之类的简单方法可以 对于非常相似或 包含可识别可区分功能类别的可识别基序 对于许多蛋白质超家族而言，成功预测远非微不足道。 SFLD中功能多样的超家族就是这种情况。 这些是执行不同化学物质的同源酶 转换，使用不同的基板，但都共享特定的 化学功能或部分反应。 SFLD的主要目的 是为了帮助研究人员策划这些类型的超家族，以 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。 因为 给定超家族中不同的功能家庭看起来相似，但是 执行不同的特定反应，它们很难注释，并且 易于误导，显示出高达80％的误声 档案数据库GenBank NR和Trembl。因为序列信息是 仍然有大量可用，需要自动化的方法 使用新确定的序列更新SFLD超家族并分配 他们到适当的功能家庭。显然，改进的方法 迫切需要实现这些功能分配。 发展方法的发展一直是 RBVI与Wake的Jacquelyn Fetrow教授合作 森林大学。 Dr.开发的主动站点分析方法 现在已经与Babbitt中开发的方法整合了Fetrow 实验室，遗传算法在结构中搜索模式：喘气，to 自动确定能够区分新的3D模板 超家族成员是为了自动分配序列的目的 到他们所属的特定功能家庭。喘息会 结合Fetrow的方法创建序列和结构基序的方法 SFLD数据的自动聚类。该方法的核心元素包括 一种被称为“模糊功能形式”的图案生成的技术，（FFF）， 由工具蛋白蛋白活性站点结构搜索（通过）和 使用三维或 基于结构的，有源位点的分析，以识别位于 活动地点周围的空间环境。 通行证使用FFF 技术，描述蛋白质功能部位 三个关键残基的α碳对功能部位很重要 相邻残基的化学和α碳。 基于 与功能相关的蛋白质应具有结构性的前提 在功能部位的相似性，通过传递返回相关蛋白 启动已知功能部位。 DASP在此上扩展，提取 在每个关键残留物附近发现的残留物 蛋白质，从这些碎片中产生基序，并使用这些片段 搜索数据库中的所有序列以返回可能共享的蛋白质 此功能。 一起使用这些工具，并以一种迭代的方式使用 提供了一种快速的方法来推测功能表征两者 结构和序列。 该项目的初步结果显示出非凡的精度 区分烯醇酶和激酶中功能多样的家族 超家族。前者是SFLD的带注释的超家族之一 这是此类自动化的挑战性测试系统 努力。