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Collaborative Research: Mathematical Framework for Biomolecules: From Protein to RNA to Chromosomes

合作研究：生物分子的数学框架：从蛋白质到RNA到染色体

基本信息

批准号：
10189648
负责人：
Jinfeng Zhang
金额：
$ 30.88万
依托单位：
FLORIDA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10189648
关键词：
Active Sites Address Affect Algorithms Area Binding Binding Proteins Bioinformatics Biological Process Cell Nucleus Chromosome Structures Chromosomes Classification Classification Scheme Communication Computer Vision Systems Detection Development Elements Epigenetic Process Family Family Planning Genomic Segment Genomics Grain Hi-C Image Analysis Individual Joints Maps Mathematics Measures Medical Imaging Membrane Proteins Methodology Methods Modeling Molecular Conformation Nature Network-based Nucleotides Outcome Pattern Pattern Recognition Probability Property Protein Analysis Protein Engineering Protein Family Proteins RNA Research Research Activity Sampling Set protein Side Statistical Models Structure Structure-Activity Relationship Surface System Testing Trees Variant Vertebral column Work base database structure design epigenomics experience flexibility genome annotation genomic data improved insight mathematical model network models novel organizational structure protein distribution protein structure shape analysis structural biology structural genomics success tool development

项目摘要

Despite rapid progress in structural bioinformatics, a rigorous and unifying mathematical and statistical framework is missing in our current toolbox for analysis, classification, and organization of individual as well as groups of biomolecules. We have recently developed such a framework based on the elastic shape analysis (ESA) for the comparison of protein and RNA structures. Under this framework, the formal geodesic distance for any two protein/RNA structures can be computed rapidly. Probability distributions can also be built for families of protein/RNA structures, and can be used to classify structures in a principled way through statistical hypothesis testing. In addition, sequence information can be naturally incorporated so that comparison of structures can be conducted in the joint sequence-structure space. We have also developed novel algorithms for matching and analyzing protein surfaces. We propose to significantly further develop these methodologies for important applications in structure biology, including studying chromosome structures by combining both 30 structure and sequence level information. The proposed research will make significant contributions to the following areas: (1) This proposal will fill an important gap in structure biology - the lack of a rigorous mathematical and statistical framework for biomolecular structure comparison; (2) Our proposed unifying framework will allow natural incorporation of sequence information for structure comparison; (3) Our approach can uncover distinct clusters at the deepest level of current classification scheme (i.e. SCOP family), enabling a finer classification of biomolecular structures. Preliminary results indicate that by using carefully measured structural similarity, we will obtain representative sets of proteins of higher quality than those by current sequence similarity based methods; (4) The probabilistic models designed for protein/RNA backbone structures and surfaces will capture the flexible nature of protein structures through the use of ensemble of conformations, while maintaining high computational efficiency. These models will also enable effective characterization of family-specific variations among proteins, an important task none of the existing methods work well; (5) Protein/RNA structures will be organized using network-based data structures using probabilistic approaches. This new organization will effectively integrates sequence, backbone structure, and surface information, facilitating discovery of novel insight; and (6) these new development will be rapidly generalized for studying chromosome structures. This proposed research will allow development of tools that will also be applicable in other areas of shape analysis, including medical image analysis, computer vision, and pattern recognition. Our work will help to increase the communication between the field of protein structure analysis and the field of shape analysis, and will stimulate more cross-over development in methodology and transform research activities in both fields.

尽管在结构生物信息学的快速进展，一个严格的和统一的数学和统计框架是在我们目前的工具箱中缺少的分析，分类和组织的个人以及团体的生物分子。我们最近开发了这样一个框架的基础上的弹性形状分析（ESA）的蛋白质和RNA结构的比较。在此框架下，任何两个蛋白质/RNA结构的正式测地线距离可以快速计算。概率分布也可以为蛋白质/RNA结构的家族构建，并且可以通过统计假设检验以原则性的方式用于对结构进行分类。此外，序列信息可以自然地并入，使得结构的比较可以在联合序列-结构空间中进行。我们还开发了用于匹配和分析蛋白质表面的新算法。我们建议显着进一步发展这些方法在结构生物学中的重要应用，包括研究染色体结构相结合的结构和序列水平的信息。该研究将在以下领域做出重大贡献：（1）该建议将填补结构生物学中的一个重要空白-生物分子结构比较缺乏严格的数学和统计框架;（2）我们提出的统一框架将允许自然地并入序列信息以进行结构比较;（3）我们的方法可以在当前分类方案（即SCOP家族）的最深层次上发现不同的簇，从而能够对生物分子结构进行更精细的分类。初步结果表明，通过仔细测量的结构相似性，我们将获得更高的质量比目前的基于序列相似性的方法的蛋白质的代表集;（4）概率模型设计的蛋白质/RNA骨架结构和表面将捕捉蛋白质结构的灵活性，通过使用构象的合奏，同时保持高的计算效率。这些模型还将使蛋白质之间的家族特异性变异的有效表征成为可能，这是一项重要的任务，现有的方法都不能很好地工作;（5）蛋白质/RNA结构将使用基于网络的数据结构，使用概率方法进行组织。这种新的组织将有效地整合序列，骨架结构和表面信息，促进发现新的见解;（6）这些新的发展将迅速推广到研究染色体结构。这项拟议的研究将允许开发的工具，也将适用于形状分析的其他领域，包括医学图像分析，计算机视觉和模式识别。我们的工作将有助于增加蛋白质结构分析领域和形状分析领域之间的交流，并将促进方法学的更多交叉发展，并改变这两个领域的研究活动。