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

描述(申请人提供)：蛋白质的结构比较对于了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很重要。尽管多年来进行了大量的研究，但它仍然是一个具有挑战性和开放性的问题。蛋白质是灵活的分子，其结构的刚性匹配，就像大多数当前方法所使用的那样，很难识别相对较远的、功能上重要的相似之处。结构比较的另一个众所周知的问题是缺乏全面的统计框架来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意义。在这个建议中，我们开发了基于弹性形状分析(ESA)的方法来进行蛋白质结构的比较和比对。ESA允许使用两个蛋白质结构的拉伸和弯曲的组合进行灵活的匹配，这是通过形式距离，测地线距离来量化的。通过高效的动态规划算法，可以得到任意两个结构之间的最佳匹配对应的最小测地线距离。可以计算结构总体的均值和协方差。可以开发严格的统计框架来进行结构比较和分类。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中特定于家族的结构变化；可以进行结构分类的假设检验。基于该框架，我们建议1)建立一个统一的统计框架，利用蛋白质结构家族的概率分布对蛋白质结构进行分类；2)发展基于一组蛋白质结构计算的平均结构的多重结构比对方法；3)发展一种在联合序列-结构空间上比对蛋白质结构的方法，将骨架几何信息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.