Elastic Shape Analysis for Protein Structure Alignment-New Advancement in an Old

描述（由申请人提供）：蛋白质的结构比较对于理解蛋白质之间的进化关系、预测蛋白质结构和预测蛋白质功能是重要的。尽管过去多年来进行了许多研究，但它仍然是一个具有挑战性和开放性的问题。蛋白质是柔性分子，并且如大多数当前方法所使用的，其结构的刚性匹配难以识别相对遥远的、功能上重要的相似性。结构比较中的另一个众所周知的问题是缺乏用于评估单个蛋白质结构和蛋白质结构类别之间相似性的统计显著性的综合统计框架。在这个提议中，我们开发了基于弹性形状分析（ESA）的蛋白质结构比较和比对方法。ESA允许使用两种蛋白质结构的拉伸和弯曲的组合进行灵活的匹配，其通过正式距离（测地线距离）来量化。最小测地线距离对应于任意两个结构之间的最佳匹配，可以通过有效的动态规划算法来获得。可以计算结构总体的均值和协方差。可以为结构比较和分类制定严格的统计框架。在这个框架下，可以评估两个结构之间的相似性;可以表征蛋白质家族内的家族特异性结构变异;可以进行结构分类的假设检验。在此基础上，我们提出：（1）建立一个统一的基于概率分布的蛋白质结构分类统计框架，（2）建立一组基于平均结构的多结构比对方法，（3）建立一组基于平均结构的多结构比对方法，（4）建立一组基于平均结构的多结构比对方法，（5）建立一组基于平均结构的多结构比对方法。以及3）开发用于在联合序列上比对蛋白质结构的方法-结构空间以将骨架几何和序列信息结合到结构比对中。

DESCRIPTION (provided by applicant): Structure comparison of proteins is important for understanding the evolutionary relationships between proteins, predicting protein structures and predicting protein functions. Despite many studies in the past over the years, it remains a challenging and open problem. Proteins are flexible molecules and rigid matching of their structures, as used by most current methods, has the difficulty of recognizing relatively distant, functionally important similarities. Another well known issue in structure comparison is the lack of comprehensive statistical framework for assessing the statistical significance of similarities between individual protein structures and classes of protein structures. In this proposal, we develop methods based on elastic shape analysis (ESA) for protein structure comparison and alignment. ESA allows flexible matching using a combination of stretching and bending of two protein structures, which is quantified by a formal distance, geodesic distance. The minimum geodesic distance, corresponding to the best matching between any two structures, can be obtained by efficient dynamic programming algorithm. Mean and covariance of a population of structures can be calculated. Rigorous statistical framework can be developed for structure comparison and classification. Under this framework, similarities between two structures can be assessed; family-specific structure variations within a protein family can be characterized; and hypothesis testing for structure classification can be conducted. Based on the framework, we propose to 1) develop a unified statistical framework for classification of protein structures usin probability distributions built from families of protein structures; 2) develop multiple structure alignment method based on the mean structure calculated for a group of protein structures; and 3) develop a method for aligning protein structures on the joint sequence-structure space to incorporate both backbone geometric and sequence information into structure alignment.