Theoretical and Computational Approaches

该计划项目的总体目标是能够预测分子功能，例如， 底物特异性和/或特定的化学反应，烯醇化酶和AH中的酶 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 结构和配体对接来帮助实现这一目标。在与实验研究人员的密切合作中，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以前表征的酶的结构和机械将作为控制评估计算结果。未知功能的建模序列将有助于选择实验结构表征和生化测试的目标;相反，从实验活性筛选与基板库的结果将被用来完善超家族序列和结构的聚类，并提供额外的限制建模和对接练习。实验解决方案的配体结构的目标是最有前途的我们的预测将是非常宝贵的对接结果和方法的评估。相反，环建模练习可能有助于解释X射线晶体学无法分辨的结构区域。我们预计，实验组和计算组之间的这种合作也将导致改进的工具和方法，用于分子功能的半自动预测，特别是对于烯醇化酶和AH超家族，并且通常用于来自基因组计划的更广泛的未知或特征不足的开放阅读框架（ORF）。

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.