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Characterization of proteins with intrinsic or unfolded state disordercomprises a new frontier in structural biology, requiring the characterizationof diverse and dynamic structural ensembles. We introduce a comprehensiveBayesian framework, the Extended Experimental Inferential StructureDetermination (X-EISD) method, that calculates the maximum log-likelihood of aprotein structural ensemble by accounting for the uncertainties of a wide rangeof experimental data and back-calculation models from structures, including NMRchemical shifts, J-couplings, Nuclear Overhauser Effects, paramagneticrelaxation enhancements, residual dipolar couplings, and hydrodynamic radii,single molecule fluorescence F\"orster resonance energy transfer efficienciesand small angle X-ray scattering intensity curves. We apply X-EISD to the drkNSH3 unfolded state domain and show that certain experimental data types aremore influential than others for both eliminating structural ensemble models,while also finding equally probable disordered ensembles that have alternativestructural properties that will stimulate further experiments to discriminatebetween them.

Extended Experimental Inferential Structure Determination Method for Evaluating the Structural Ensembles of Disordered Protein States