Protein Dynamics to Define and Refine Disordered Protein Ensembles

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Intrinsically disordered proteins (IDPs) and unfolded proteins exhibit dynamic conformational ensembles crucial for biological function.
• Protein dynamics and conformational sampling are vital but time-dependent data are underutilized in refining disordered protein models.

Purpose of the Study:

• To develop a computational framework for generating dynamic disordered protein ensembles.
• To integrate NMR-derived dynamics parameters into ensemble generation.
• To improve the accuracy of disordered protein models using time-dependent data.

Main Methods:

• Utilized an elastic network model and normal-mode displacements for ensemble generation.
• Incorporated NMR-derived dynamics parameters such as transverse R2 relaxation rates and Lipari-Szabo order parameters (S2).
• Applied the framework to the unfolded state of the drkN SH3 domain.

Main Results:

• Generated a dynamic disordered ensemble consistent with NMR dynamics data.
• Demonstrated that the dynamic ensemble provides superior agreement with experimental validation data compared to a static ensemble.
• Validated the approach against diverse experimental data including NMR chemical shifts, J-couplings, NOEs, PREs, RDCs, and SAXS.

Conclusions:

• The developed computational framework effectively generates dynamic disordered protein ensembles.
• Dynamic ensembles offer a more accurate representation of protein disorder than static models.
• This approach enhances the integration of experimental dynamics data for refining protein structural models.