  Characterization of proteins with intrinsic or unfolded state disorder
comprises a new frontier in structural biology, requiring the characterization
of diverse and dynamic structural ensembles. We introduce a comprehensive
Bayesian framework, the Extended Experimental Inferential Structure
Determination (X-EISD) method, that calculates the maximum log-likelihood of a
protein structural ensemble by accounting for the uncertainties of a wide range
of experimental data and back-calculation models from structures, including NMR
chemical shifts, J-couplings, Nuclear Overhauser Effects, paramagnetic
relaxation enhancements, residual dipolar couplings, and hydrodynamic radii,
single molecule fluorescence F\"orster resonance energy transfer efficiencies
and small angle X-ray scattering intensity curves. We apply X-EISD to the drkN
SH3 unfolded state domain and show that certain experimental data types are
more influential than others for both eliminating structural ensemble models,
while also finding equally probable disordered ensembles that have alternative
structural properties that will stimulate further experiments to discriminate
between them.
