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.
