Joint Center for Molecular Modeling

分子模拟联合中心

• 批准号: 7021035
• 负责人: Adam Godzik
• 金额: $ 70万
• 依托单位: SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7021035
• 关键词: biochemical evolution; chemical models; classification; computational biology; computer simulation; conformation; data quality /integrity; high throughput technology; mathematical model; mathematics; model design /development; protein folding; protein sequence; protein structure function

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].

描述（由申请人提供）：基于图理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碎片的重排。最近，同样的工具还提供了多个序列一致性的重大突破。在这里，我们建议将这些工具应用于蛋白质结构分析，并将所得的见解对蛋白质结构的演变进行，以提高比较建模中的模型质量。 遥远同源物之间的结构比较清楚地表明，结构比较中的主要范式可以将蛋白质结构分为不变的核心，而柔性环会分解低于40％-50％-50％的序列身份阈值。取而代之的是，在结构中的任何地方都可以进行重大重新分组，其中二级结构要素正在经历重大的变化和运动。结果，基于在刚性核心结构上安装的序列安装的比较建模中的标准方案必须失败。 同源物之间的结构差异，整个折叠过程也通过系统的自由能进行了驱动，但是由于当前力场和计算方法的严重缺陷，基于能量的对此类变化的预测并不成功。在这笔赠款中，我们建议通过首先发现比较建模的质量，然后应用蛋白质结构变化的经验规则。已知蛋白质结构数量的快速增长，部分是由于蛋白质结构倡议带头的高吞吐量结构确定的技术进步，从而越来越密集地覆盖了许多折叠的结构空间。这为发现这种经验规则提供了丰富的学习基础，只要可以开发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/柔性环后的下一个近似值中，蛋白质结构可以描述为是由刚性子域构建的，这些子域的简单重排占了遥远同源物之间结构差异的几乎一半。此外，蛋白质只能采用位于整个构象空间的特定低维度子空间中的结构。为了从比较建模中提高模型的质量，我们计划确定所有已知褶皱的保守子域，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，我们将使用此信息来生成模板结构的可能变体，并使用模型评估工具来识别与[SIC]最相似的一种。