Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

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) m...

Full description

Saved in:
Bibliographic Details
Published in:Communications chemistry 2020-01, Vol.3 (1), Article 74
Main Authors: Lincoff, James, Haghighatlari, Mojtaba, Krzeminski, Mickael, Teixeira, João M. C., Gomes, Gregory-Neal W., Gradinaru, Claudiu C., Forman-Kay, Julie D., Head-Gordon, Teresa
Format: Article
Language:English
Subjects:
101/6
631/45/56
631/535/1267
82
Bayesian analysis
Biology
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Couplings
Energy transfer
Fluorescence
NMR
Nuclear magnetic resonance
Optimization
Proteins
Resonance scattering
Small angle X ray scattering
Structural analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 Förster 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. Characterising ensembles and populations of disordered structures is a challenge in structural biology. Here a Bayesian model allows solution data from NMR, fluorescence, and SAXS experiments to be synthesised in order to quantify the conformational distribution of disordered protein states.
ISSN:2399-3669
2399-3669
DOI:10.1038/s42004-020-0323-0