Bayesian analysis of structural shape and conformation

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，结构分析方法依赖于生物物理上不切实际和限制性的最小二乘准则来寻找最佳叠加。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，这项提议的工作将极大地扩展我们精确叠加、对齐和分析结构构象的能力。这项工作的具体目标是：(1)开发用于叠加结构构象的贝叶斯模型和理论，(2)开发基于多结构的对齐的ML和贝叶斯模型和理论，以及(3)开发和分发实现该ML和贝叶斯结构分析的计算工具。ML和贝叶斯结构分析将比当前的最小二乘和其他特别方法提供许多明显的优势，包括(1)直接估计估计参数解中的不确定性，(2)优雅地处理结构数据中的不确定性，(3)自然地结合不同类型的先验结构和分子信息，(4)易于检查结构变化和进化的复杂模型，(4)简单的结构变化和进化模型。(5)对复杂结构假设进行严格检验；(6)对缺失结构数据进行自然处理。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，还会影响异常广泛的科学领域，包括任何比较物体形状和构象的领域。这项工作的结果将适用于任何可以表示为多维空间中的笛卡尔点集的实体，无论所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剖面。

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.