SEI(BIO): Integration of Multimodal Experiments for Protein Structure

SEI(BIO)：蛋白质结构多模式实验的整合

• 批准号: 0430788
• 负责人: Christopher Bailey-Kellogg
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2004-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430788&HistoricalAwards=false
• 关键词: SEI; BIO; Integration; Multimodal; Experiments

Determination of three-dimensional structures of proteins provides insight into their evolutionary origins, functions, and mechanisms. While determining atomic-detail structures by traditional methods can be expensive, time consuming, or even infeasible, coarser-grained structural characterization is often sufficient to provide significant insight. The multimodal approach to rapid, approximate protein structure integrates complementary experimental evidence from a number of sources in order to verify and discriminate among computationally predicted structures. Appropriate experiments, due to their speed, variety of information, and disjoint experimental limitations, include cross-linking, giving rough distance restraints; stability assays after mutagenesis, characterizing structural roles of residues in particular environments; and solution x-ray scattering, yielding global shape properties.The multimodal approach is grounded in probabilistic models that evaluate consistency of data with structural features (distances, accessibilities, overall shapes). This approach takes advantage of the diversity and relative independence of the available methods, rather than seeking to integrate separate measurements into an overarching physical model. Inference algorithms then reason about posterior distributions of structures and features, avoiding false optimism in a single answer, measuring overall plausibility, assessing the available information content, and quantitating uncertainty in individual features. Associated experiment planning algorithms optimize multimodal experiments (e.g. selecting cross-linker length and specificity, and identifying optimal mutation sites) for a given analysis task, so as to efficiently utilize experimental resources while maximizing information gain. The multimodal integration mechanism is being developed, applied, and tested with published data from individual methods, and is being used to plan and interpret appropriate experiments for selected proteins of unknown structure. Active participation of graduate students expands educational activities and tools will be accessible.

蛋白质三维结构的确定提供了对其进化起源、功能和机制的洞察。虽然传统方法确定原子细节结构可能昂贵、耗时，甚至是不可行的，但粗粒度的结构表征通常足以提供重要的洞察力。快速、近似的蛋白质结构的多模式方法集成了来自多个来源的补充实验证据，以验证和区分计算预测的结构。由于其速度、信息的多样性和不相交的实验限制，适当的实验包括交联，给出粗略的距离限制；诱变后的稳定性分析，表征特定环境中残基的结构角色；以及溶液X射线散射，产生全局形状属性。多模式方法基于概率模型，评估数据与结构特征(距离，可获得性，整体形状)的一致性。这种方法利用了现有方法的多样性和相对独立性，而不是试图将单独的测量结果集成到一个总体物理模型中。然后，推理算法对结构和特征的后验分布进行推理，避免在单一答案中出现虚假乐观，测量总体可信程度，评估可用信息含量，并量化个别特征中的不确定性。相关的实验规划算法针对给定的分析任务优化多模式实验(如选择交联剂的长度和专一性，以及确定最佳突变位点)，从而在最大化信息收益的同时有效地利用实验资源。多模式整合机制正在开发、应用和测试中，并使用已公布的单个方法的数据进行测试，并用于为结构未知的选定蛋白质计划和解释适当的实验。研究生的积极参与扩大了教育活动和工具的可获得性。