Journal
COMMUNICATIONS CHEMISTRY
Volume 3, Issue 1, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s42004-020-0323-0
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Funding
- National Institutes of Health [5R01GM127627-03]
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2016-06718]
- Canada Research Chairs program
- Natural Sciences and Engineering Research Council of Canada [RGPIN 2017-06030]
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
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Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. Here we introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, which calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R-h), single molecule fluorescence Forster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.
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