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

蛋白质三维结构的测定有助于深入了解其进化起源、功能和机制。虽然通过传统方法确定原子细节结构可能是昂贵的，耗时的，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。 多模态方法快速，近似的蛋白质结构集成了互补的实验证据，从一些来源，以验证和区分计算预测的结构。 适当的实验，由于其速度，信息的多样性，和不相交的实验限制，包括交联，给出粗略的距离限制;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，产生全球形状属性。多模态方法是基于概率模型，评估数据与结构特征的一致性（距离、辅助设施、整体形状）。 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整合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，避免单一答案中的错误乐观，测量整体可解释性，评估可用信息内容，并量化单个特征中的不确定性。 相关的实验规划算法优化给定分析任务的多模态实验（例如，选择交联剂长度和特异性，以及识别最佳突变位点），以便有效地利用实验资源，同时最大化信息增益。 多模式整合机制正在开发，应用和测试与已公布的数据从个别的方法，并被用来计划和解释适当的实验，为选定的蛋白质的未知结构。 研究生的积极参与扩大了教育活动，并将提供工具。

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.