Speaker
Description
Small-angle scattering (SAS) uses x-ray or neutron scattering at small angles to investigate the structure of materials at the scale about 1-100nm. SAS is uniquely suited to study the conformational ensembles adopted by multidomain proteins. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence, followed by a complete Bayesian inference of population weights.
