Theoretical and Computational Approaches

理论和计算方法

• 批准号: 6854963
• 负责人: PATRICIA CLEMENT BABBITT
• 金额: $ 51.36万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6854963
• 关键词: X ray crystallography; active sites; aminohydrolases; biochemical evolution; biomedical automation; chemical bond; chemical reaction; chemical structure function; combinatorial chemistry; computer simulation; enzyme activity; enzyme substrate; high throughput technology; method development; phosphopyruvate hydratase

The overall goal of this Program Project is to enable prediction of the molecular functions, e.g., substrate specificity and/or specific chemical reaction, of enzymes in the enolase and AH superfamilies. The role of the computational project is to integrate and apply bioinformatic characterization of sequences, structures, and functions, comparative modeling of protein structures, and ligand docking to help achieve this goal. In close collaboration with the experimental investigators, we envision an iterative cycle providing multiple parallel and serial paths to obtaining high quality information useful for functional prediction. Applying our approaches to enzymes previously characterized structurally and mechanistically will serve as controls for evaluating computational results. Modeling sequences of unknown function will aid in the selection of targets for experimental structural characterization and biochemical testing; conversely, results from experimental activity screening with libraries of substrates will be used to refine clustering of superfamily sequences and structures, and to provide additional restraints for modeling and docking exercises. Experimental solution of liganded structures targeted by the most promising of our predictions will be invaluable for the evaluation of docking results and methodologies. Conversely, loop modeling exercises may aid in interpreting regions of structures that cannot be resolved by x-ray crystallography. We expect that this collaboration between the experimental and computational groups will also result in improved tools and methodologies for semi-automated prediction of molecular function, specifically for the enolase and AH superfamilies, and generally for the wider set of unknown or under-characterized open reading frames (ORFs) coming out of the genome projects.

该计划项目的总体目标是能够预测分子功能，例如， 烯醇化酶和 AH 中酶的底物特异性和/或特定化学反应 超家族。计算项目的作用是整合和应用生物信息学 序列、结构和功能的表征，蛋白质的比较建模 结构和配体对接有助于实现这一目标。通过与实验研究人员密切合作，我们设想了一个迭代循环，提供多个并行和串行路径，以获得对功能预测有用的高质量信息。将我们的方法应用于先前在结构和机械上表征的酶将作为评估计算结果的对照。对未知功能的序列进行建模将有助于选择实验结构表征和生化测试的靶标；相反，底物文库的实验活性筛选结果将用于细化超家族序列和结构的聚类，并为建模和对接练习提供额外的限制。我们最有希望的预测所针对的配体结构的实验解决方案对于对接结果和方法的评估将具有无价的价值。相反，循环建模练习可能有助于解释 X 射线晶体学无法解析的结构区域。我们期望实验组和计算组之间的合作也将带来改进的工具和方法，用于分子功能的半自动预测，特别是烯醇化酶和 AH 超家族，以及更广泛的未知或未充分表征的开放阅读来自基因组计划的框架（ORF）。