Inverse Problem for Estimating Structure of Biological Macromolecules from SAXS

利用 SAXS 估计生物大分子结构的反问题

• 批准号: 8045564
• 负责人: Jay Breidt
• 金额: $ 28.08万
• 依托单位: COLORADO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-20 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8045564
• 关键词: Biochemistry; Biological; Complex; Computing Methodologies; Data; Data Set; Development; Disease; Health; Investigation; Macromolecular Complexes; Methods; Modeling; Molecular Structure; Pattern; Pharmaceutical Preparations; Property; Proteins; Research; Research Project Grants; Resolution; Roentgen Rays; Science; Scientist; Solutions; Structure; System; Technology; Uncertainty; X-Ray Crystallography; falls; human disease; macromolecular assembly; macromolecule; mathematical model; multidisciplinary; novel; protein function; public health relevance; reconstruction; structural biology; tool

DESCRIPTION (provided by applicant): Technologies to rapidly obtain information about the structure of macromolecular complexes are needed to understand how these complexes function and, in particular, how aberrant interactions between molecules may result in human disease. Small-angle X-ray scattering is a relatively simple experimental method to obtain low-resolution information that offers advantages over methods such as X-ray crystallography and NMR. However, recovering information about the structure of a molecule from a scattering pattern is an extremely ill-posed and ill-conditioned mathematical inverse problem. In addition, evaluating mathematical models for a scattering pattern requires a computationally demanding, high dimensional integration. As a consequence of these difficulties, the current state of practice in SAXS reconstruction of molecular structure falls short of its potential as a scientific tool. The proposed research project is a multidisciplinary investigation into the inverse problem of determining structural information about complex bio-molecules from SAXS scattering patterns. In a close, well-established partnership with biochemists using SAXS in their research, a team of computational scientists, mathematicians, and statisticians will undertake a systematic investigation of the mathematical properties of the SAXS inverse problem using new statistical and mathematical tools, devise novel computational methods for computing solutions of the inverse problem for specific data sets, undertake an analysis quantifying the effects of model uncertainty, experimental error, and computational error on identifications made using SAXS data, and apply these methods to biomedically relevant experimental systems. The project is driven by specific research problems in biochemistry and structural biology, where extracting all available information from experimental data is essential to revealing low-resolution information for large macromolecules or complexes in solution. Such information advances understanding of the function of proteins and macromolecular assemblies in health and disease. PUBLIC HEALTH RELEVANCE: The research will have a positive impact on the science of biochemistry through the construction of robust tools for identification of structure of macromolecules from small angle X-ray scattering data. Obtaining structural information of proteins and macromolecular assemblies is key to understanding their function in health and disease. This information opens the potential to the development of more effective drugs.

描述（由申请人提供）：需要快速获取有关大分子复合物结构信息的技术，以了解这些复合物如何发挥作用，特别是分子之间的异常相互作用如何导致人类疾病。小角 X 射线散射是一种相对简单的获得低分辨率信息的实验方法，与 X 射线晶体学和核磁共振等方法相比具有优势。然而，从散射图案中恢复有关分子结构的信息是一个极其不适定和病态的数学逆问题。此外，评估散射图案的数学模型需要计算要求高的高维积分。由于这些困难，目前 SAXS 重建分子结构的实践状况还不足以发挥其作为科学工具的潜力。 拟议的研究项目是对从 SAXS 散射模式确定复杂生物分子结构信息的逆问题的多学科研究。通过与在研究中使用 SAXS 的生物化学家建立密切、良好的合作伙伴关系，由计算科学家、数学家和统计学家组成的团队将使用新的统计和数学工具对 SAXS 反问题的数学特性进行系统研究，设计新颖的计算方法计算特定数据集反问题解的方法，进行量化模型不确定性、实验误差和计算误差对使用 SAXS 数据进行识别的影响的分析，并将这些方法应用于生物医学相关领域实验系统。该项目是由生物化学和结构生物学的具体研究问题驱动的，其中从实验数据中提取所有可用信息对于揭示溶液中大分子或复合物的低分辨率信息至关重要。这些信息促进了对蛋白质和大分子组装体在健康和疾病中的功能的理解。 公共健康相关性：该研究将通过构建强大的工具来从小角度 X 射线散射数据识别大分子结构，从而对生物化学科学产生积极影响。获取蛋白质和大分子组装体的结构信息是了解它们在健康和疾病中的功能的关键。这些信息开启了开发更有效药物的潜力。