Joint Center for Molecular Modeling

描述(由申请人提供)：基于图论的新工具彻底改变了基因组分析，提供了更好的方法来识别和分类基因组片段的重排。同样的工具最近也在多序列比对方面取得了重大突破。在这里，我们建议将这些工具应用于蛋白质结构分析，并使用由此产生的对蛋白质结构进化的见解来提高比较建模中的模型质量。 远距离同源物的结构比较清楚地表明，结构比较中的主导范式，即蛋白质结构可以被划分为不变的核心和柔性环，其序列一致性阈值低于40%-50%。相反，结构中的任何地方都可能发生重大的重新排列，次级结构元素经历显著的移位和移动。因此，基于刚性核心结构上的序列安装的比较建模中的标准方案对于这样的同源物来说肯定是失败的。 同系物之间的结构差异，就像整个折叠过程一样，是由系统的自由能驱动的，但由于目前力场和计算方法的严重缺陷，基于能量的预测这种变化是不成功的。在这项授权中，我们建议通过首先发现并应用蛋白质结构变化的经验规则来提高比较建模的质量。已知蛋白质结构数量的快速增长在一定程度上受到蛋白质结构倡议带头推动的高通量结构确定技术进步的推动，导致对许多折叠的结构空间的覆盖越来越密集。这为发现这种经验规律提供了丰富的学习基础，前提是可以开发出描述蛋白质结构变化的正确形式主义。 在初步分析中，我们已经表明，在不变的核心/柔性环之后的下一次近似中，蛋白质结构可以描述为由刚性亚域建立的，这些亚域的简单重排几乎解释了远距离同源物之间结构差异的一半。此外，蛋白质只能采用位于整个构象空间的特定低维空间中的结构。为了提高比较模型的质量，我们计划识别所有已知折叠的保守亚域，并通过分析这些折叠的已知结构来描述允许的子空间。在下一步中，我们将使用这些信息来生成模板结构的可能变体，并使用模型评估工具来确定与[SIC]最相似的一个。

DESCRIPTION (provided by applicant): New tools based on graph theory have revolutionized genome analyses, providing better ways to identify and classify rearrangements of genomic fragments. The same tools recently also provided a major breakthrough in multiple sequence alignment. Here we propose to apply these tools to protein structure analysis and use the resulting insights into protein structure evolution to increase model quality in comparative modeling. Structure comparison between distant homologs show clearly that the dominant paradigm in structure comparison, that a protein structure could be divided into an invariant core and flexible loops breaks down below 40%-50% sequence identity threshold. Instead, significant rearrangements can happen anywhere in the structure, with secondary structure elements undergoing significant shifts and movements. As a result, standard protocol in comparative modeling, based on sequence mounting on a rigid core structure, must fail for such homologs. Structural differences between homologs are driven, as is the entire folding process, by free energy of the system, but because of serious deficiencies in current force fields and computational approaches, energy-based predictions of such changes are not successful. In this grant we propose to improve the quality of comparative modeling by first discovering and then applying empirical rules of protein structure changes. Rapid growth of the number of known protein structures, fueled in part by technical advances in high throughput structure determination spearheaded by the Protein Structure Initiative, resulted in increasingly dense coverage of the structural space of many folds. This provides a rich learning base to discover such empirical rule, provided a right formalism to describe protein structure changes can be developed. In preliminary analyses we have shown that in a next approximation after the invariant core/flexible loops, protein structure can be described as built from rigid subdomains, and simple rearrangements of these subdomains account for almost half of the structural differences between distant homologs. Moreover, proteins can only adopt structures lying in a specific low dimensionality subspace of the entire conformational space. To improve the quality of models from comparative modeling, we plan to identify conserved subdomains for all known folds and to describe the allowed subspaces by analyzing already known structures from these folds. In the next step we will use this information to generate possible variants of the template structure and use model evaluation tools to identify the one most similar to the [sic].