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: 0502801
• 负责人: Christopher Bailey-Kellogg
• 金额: --
• 依托单位: Dartmouth College
• 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 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0502801&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.

蛋白质的三维结构的确定提供了对其进化起源，功能和机制的见解。尽管通过传统方法确定原子段结构可能是昂贵的，耗时甚至是不可行的，但更粗糙的结构表征通常足以提供重要的见解。 快速，近似蛋白质结构的多模式方法整合了来自许多来源的互补实验证据，以验证和区分计算预测的结构。 由于速度，各种信息和不相交的实验限制，适当的实验包括交联，提供粗糙的距离约束；诱变后的稳定性测定，表征残基在特定环境中的结构作用；和解决方案X射线散射，产生全局形状的属性。多模式方法基于概率模型，这些模型评估具有结构特征（距离，可访问性，整体形状）的数据一致性。 这种方法利用了可用方法的多样性和相对独立性，而不是试图将单独的测量值集成到总体物理模型中。 然后，推论算法是关于结构和特征的后验分布的原因，避免了单个答案中的错误乐观，衡量了整体合理性，评估可用信息内容并量化单个特征的不确定性。 相关的实验计划算法优化了多模式实验（例如，选择交联长度和特异性，并确定最佳的突变位点）为给定的分析任务，以便有效利用实验资源，同时最大程度地利用了信息增益。 正在开发，应用，应用和测试多模式的集成机制，并使用来自各个方法的已发布数据，并用于计划和解释适当的实验，以实验未知结构的选定蛋白质。 研究生的积极参与将扩大教育活动和工具。