Deciphering Enzyme Specificity: Theoretical and Computational Approaches

破译酶特异性：理论和计算方法

• 批准号: 7743890
• 负责人: PATRICIA CLEMENT BABBITT
• 金额: $ 48.25万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7743890
• 关键词: Active Sites; Address; Algorithms; Amidohydrolases; Architecture; Biochemical; Bioinformatics; Biological; Biological Models; Biological Process; Biology; Charge; Chemicals; Collaborations; Computer Simulation; Computing Methodologies; Development; Disease; Docking; Electrostatics; Enzymes; Evaluation; Evolution; Exhibits; Family; Funding; Genome; Goals; Homology Modeling; Instruction; Knowledge; Ligand Binding; Ligands; Link; Metabolic; Metabolic Pathway; Metals; Methodology; Methods; Mining; Modeling; Molecular; Molecular Conformation; Operon; Pathway interactions; Pattern; Physics; Positioning Attribute; Principal Investigator; Proteins; Protocols documentation; Publishing; Reaction; Research; Research Personnel; Ribulose-Bisphosphate Carboxylase; Sampling; Screening procedure; Sequence Determination; Solutions; Specificity; Structural Models; Structure; Substrate Specificity; Testing; base; comparative; comparison group; drug discovery; enolase; enzyme substrate; flexibility; improved; innovation; member; novel strategies; protein function; small molecule; success

This research plan describes the computational aspects of a strategy for predicting the substrate specificities of unknown enzymes from the genome projects in order to direct and facilitate experimental assignment of their functions. Anchored by functional predictions that are validated by the experimental projects, high- quality functional annotations can then be made for many additional sequences by annotation transfer. Focusing on non-trivial problems in function prediction, we have integrated our various expertises in bioinformatics, in silico clocking, and comparative structural modeling to achieve substantial success, contributing to the discovery of 32 new functions in the large and functionally diverse enolase and amidodhydrolase (AH) superfamilies, and annotation of hundreds of orthologous sequences by annotation transfer. In close collaboration with the experimental investigators, we will continue to develop an iterative cycle in which multiple parallel and serial paths are integrated to obtain high quality information useful for functional prediction. We aim in the next funding period to build on breakthroughs in docking against both experimentally determined and modeled structures, especially, to predict the functions of proteins in metabolic pathways in which these superfamily members (and those of a new target superfamily, the RuBisCO-like proteins) reside, thereby extending our efforts toward a more general solution for prediction of functional specificity. Proteins in these operons are expected to catalyze reactions in the pathway that can be linked to the fundamental chemical capabilities of our target superfamily members that are members of those operons, providing clues for metabolic context. Similarly, we can expect substrates for enzymes in the pathway to contain substructures related to those of our target superfamilies, providing additional clues for filtering docking results against these proteins. To take advantage of these similarities, new methods developed by our groups for comparison of ligand structures and substructures will be applied to docking hit lists to identify patterns in multiple proteins of an operon useful for restricting potential substrates for further evaluation and experimental testing. To the extent we succeed, this effort will lay the groundwork for generalization of our approaches for the discovery of new enzyme functions, new pathways, and new biology. RELEVANCE (See instructions): Accurate prediction of molecular function for sequences in the genome projects is required to identify mechanisms of disease and improve drug discovery and development. In collaboration with the experimental projects, continuation of this computational project will contribute to this goal by applying orthogonal methods to correctly predict molecular function in large enzyme superfamilies and to extend those predictions on a large scale to determine the biological function of associated metabolic pathways.

该研究计划描述了用于预测底物特性的策略的计算方面 从基因组项目中提取未知的酶，以便指导和促进实验任务 它们的功能。以实验项目验证的功能预测为基础，高- 然后，可以通过注释转移为许多附加序列进行高质量的功能注释。 专注于函数预测中的非平凡问题，我们整合了我们在 生物信息学，在电子计时和比较结构建模方面取得实质性成功， 有助于发现大的和功能多样化的烯醇化酶中的32个新功能 氨基水解酶(AH)超家族及其对数百条同源序列的注释 调职。在与实验研究人员的密切合作下，我们将继续开发迭代 一种综合多条并行和串行路径以获得高质量信息的周期。 功能预测。我们的目标是在下一个资金阶段，在对接这两个方面取得突破 实验确定和模拟的结构，特别是用来预测蛋白质的功能。 这些超家族成员(以及一个新的目标超家族成员，即 Rubisco类蛋白质)的存在，从而扩展了我们对预测的更一般解决方案的努力 功能专一性。这些操纵子中的蛋白质有望催化途径中的反应，从而 与我们的目标超家族成员的基本化学能力相关联 这些操纵子，为新陈代谢提供了线索。同样，我们可以期待酶的底物在 包含与我们目标超家族的亚结构相关的亚结构的途径，为 根据这些蛋白质过滤对接结果。为了利用这些相似性，新的方法 由我们的团队开发的用于比较配体结构和亚结构的将应用于对接HIT 用于识别操纵子的多个蛋白质中的模式的列表，可用于进一步限制潜在底物 评估和实验测试。就我们成功的程度而言，这一努力将为 概括我们发现新酶功能、新途径和新方法的方法 生物学。 相关性(请参阅说明)： 需要对基因组计划中的序列的分子功能进行准确的预测 识别疾病机制，改进药物发现和开发。与 实验项目，此计算项目的继续将通过应用 正确预测大酶超家族分子功能的正交法及其推广 这些预测将大规模地确定相关代谢途径的生物学功能。