Protein Dynamics to Define and Refine Disordered Protein Ensembles

Abstract

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMRderived dynamics parameters, including transverse R 2 relaxation rates and Lipari−Szabo order parameters (S 2 values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Forster resonance energy transfer, and small-angle X-ray scattering.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States

“… Finally, the drkN SH3 domain exists in dynamic equilibrium between folded and unfolded states, with the unfolded state extensively studied as a model disordered protein for the development of ensemble calculation methods due to the large number of experimental NMR, SAXS, and smFRET restraints available and its small size (59 aa). 22 , 50 , 66 , 70 …”

“… 22 , 23 We have also introduced new data types, NMR R 2 relaxation rates and S 2 order parameters, for the selection of an IDP ensemble consistent with NMR dynamics data. 50 Given the ensembles created with IDPConformerGenerator, the X-EISD model can be used as a scoring function to reweight the IDP ensembles for the best agreement with experimental data based on the different experimental and back-calculation uncertainties.…”

IDPConformerGenerator: A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States

“…We use the first 441 residues as a test system since a fragment encompassing these residues has been studied using NMR 64,65 . Short Tau peptides have also been studied 15 and we similarly utilize a Tau peptide as a test system. Finally, the drkN SH3 domain exists in a dynamic equilibrium between folded and unfolded states, with the unfolded state extensively studied as a model disordered protein for development of ensemble calculation methods due to the large number of experimental NMR, SAXS and smFRET restraints available and its small size (59 residues) 22,46,62,66 .…”

“…These data include NMR chemical shifts, J-couplings, residual dipolar couplings (RDCs), hydrodynamic radii, nuclear Overhauser effects (NOEs), and para-magnetic resonance enhancements (PREs), smFRET, and SAXS curves. 22,23 We also have introduced new data types, R2 relaxation rates and S 2 order parameters, for the selection of an IDP ensemble consistent with NMR dynamics data 46 . Given the ensembles created with IDPConformerGenerator, the X-EISD model can be used as a scoring function that helps reweight the IDP ensembles for best agreement with experimental data given the different experimental and back-calculation uncertainties.…”

“…• Finally, the drkN SH3 domain exists in a dynamic equilibrium between folded and unfolded states, with the unfolded state extensively studied as a model disordered protein for development of ensemble calculation methods due to the large number of experimental NMR, SAXS and smFRET restraints available and its small size (59 residues) 22,46,62,66 .…”