Optimizing representations for integrative structural modeling using Bayesian model selection

Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis.  But currently, the choice is usually made ad hoc, manually. Here, we have deposited NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. We have also deposited a benchmark of four macromolecular assemblies which was used to assess the performance of NestOR.