Bayesian analysis of structural shape and conformation

结构形状和构象的贝叶斯分析

• 批准号: 8309091
• 负责人: Douglas Lowell Theobald
• 金额: $ 28.72万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-05 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8309091
• 关键词: Algorithms; Amino Acid Sequence; Bayesian Analysis; Bayesian Method; Bioinformatics; Biological; Code; Communities; Complex; Computer software; Data; Databases; Development; Elements; Evolution; Homologous Protein; Human; Individual; Least-Squares Analysis; Libraries; MRI Scans; Measures; Methods; Modeling; Molecular; Molecular Conformation; Motion; Programming Languages; Proteins; Pythons; Research; Sampling; Shapes; Solutions; Source Code; Statistical Models; Structural Models; Structure; Techniques; Testing; Uncertainty; Work; abstracting; base; computerized tools; cranium; density; improved; interest; macromolecule; novel; programs; protein folding; psychologic; public health relevance; structural biology; theories; web site

DESCRIPTION (provided by applicant): Accurate analysis of structural differences and commonalities is of fundamental importance for understanding the structure, function, and evolution of biological macromolecules. For the past 40 years, structural analysis methods have relied on the biophysically unrealistic and restrictive least-squares criterion to find optimal superpositions. By developing probabilistic models of structural change that can take advantage of powerful maximum likelihood (ML) and Bayesian techniques, this proposed work will greatly expand our abilities to accurately superposition, align, and analyze structural conformations. The specific aims of this work are (1) to develop Bayesian models and theory for superpositioning structural conformation, (2) develop ML and Bayesian models and theory for multiple structure-based alignment, and (3) develop and distribute computational tools that implement this ML and Bayesian structural analysis. ML and Bayesian structural analysis will provide many distinct advantages over current least-squares and other ad hoc methods, including (1) straight- forward estimates of the uncertainty in the solutions of estimated parameters, (2) elegant handling of uncertainty in structural data, (3) natural incorporation of disparate types of prior structural and molecular information, (4) easy examination of complex models of structural change and evolution, (5) rigorous testing of complex structural hypotheses, and (6) natural handling of missing structural data. While we concentrate specifically on the conformations of macromolecules, the methods proposed herein have broad mathematical generality and will impact not only molecular structural biology but also an unusually wide range of scientific fields, including any that compare the shapes and conformations of objects. The results developed from this work will be applicable to any entity that can be represented as a set of Cartesian points in a multi-dimensional space, whether the particular structures under study are proteins, skulls, MRI scans, geological strata, or even psychological profiles of human individuals. PUBLIC HEALTH RELEVANCE: Measuring, analyzing, and comparing the shapes and conformations of the structures of objects is of fundamental importance in many diverse scientific fields. Our particular focus is the development of likelihood and Bayesian methods for the comparison and analysis of multiple three-dimensional macromolecules. While we concentrate specifically on the conformations of macromolecules, the methods proposed herein will be generally applicable to any entity that can be represented as a set of Cartesian points in a multi-dimensional space, whether the particular structures under study are proteins, skulls, MRI scans, geological strata, or even psychological profiles of human individuals.

描述（由申请人提供）：准确分析结构差异和共性对于理解生物大分子的结构、功能和进化至关重要。在过去的40年里，结构分析方法一直依赖于生物医学上不切实际和限制性的最小二乘标准来寻找最佳叠加。通过开发结构变化的概率模型，可以利用强大的最大似然（ML）和贝叶斯技术，这项工作将大大扩展我们准确叠加，对齐和分析结构构象的能力。这项工作的具体目标是（1）开发贝叶斯模型和理论用于叠加结构构象，（2）开发ML和贝叶斯模型和理论用于基于多结构的比对，以及（3）开发和分发实现ML和贝叶斯结构分析的计算工具。ML和贝叶斯结构分析将提供优于当前最小二乘法和其他特设方法的许多明显优势，包括（1）估计参数的解中的不确定性的直接估计，（2）结构数据中的不确定性的优雅处理，（3）不同类型的先前结构和分子信息的自然并入，（4）结构变化和演化的复杂模型的简单检验，（5）复杂结构假设的严格检验，（6）缺失结构数据的自然处理。虽然我们特别关注大分子的构象，但本文提出的方法具有广泛的数学通用性，不仅会影响分子结构生物学，还会影响广泛的科学领域，包括比较物体形状和构象的任何领域。从这项工作中开发的结果将适用于任何可以表示为多维空间中的一组笛卡尔点的实体，无论研究中的特定结构是蛋白质，头骨，MRI扫描，地质地层，甚至是人类个体的心理特征。 公共卫生关系：测量，分析和比较物体结构的形状和构象在许多不同的科学领域中具有根本的重要性。我们特别关注的是发展的可能性和贝叶斯方法的比较和分析多个三维大分子。虽然我们特别关注大分子的构象，但本文提出的方法通常适用于可以表示为多维空间中的一组笛卡尔点的任何实体，无论研究的特定结构是蛋白质，头骨，MRI扫描，地质地层，甚至是人类个体的心理概况。