Local structure propensities in disordered proteins from cross-correlated NMR spin relaxation

Area of Science:

• Biophysics
• Structural Biology
• Computational Biology

Background:

• Determining the structure of intrinsically disordered proteins (IDPs) is challenging due to their dynamic and heterogeneous nature.
• Experimental methods often provide averaged information, obscuring the full conformational landscape.
• Cross-correlated relaxation (CCR) rates offer complementary geometric insights but are complex to interpret due to combined structural and dynamic information.

Purpose of the Study:

• To adapt and apply cross-correlated relaxation (CCR) rates for characterizing the structure propensities of intrinsically disordered proteins (IDPs).
• To investigate the utility of computational spectroscopy and molecular dynamics (MD) simulations for calculating and interpreting CCR rates in IDPs.
• To develop a framework for inferring conformational distributions from CCR data in disordered systems.

Main Methods:

• Utilized computational spectroscopy to calculate CCR rates from molecular dynamics (MD) simulations.
• Adjusted the standard CCR analysis framework to incorporate local dynamics by including an additional CCR rate.
• Compared calculated CCR rates with conformational ensembles directly observed in MD trajectories.

Main Results:

• Demonstrated that CCR rates, when analyzed appropriately, can identify and characterize structure propensities in disordered proteins.
• Showed that accounting for local dynamics is crucial for accurate CCR interpretation in IDPs.
• Validated the computational approach by correlating calculated CCR rates with simulated protein conformations.

Conclusions:

• Cross-correlated relaxation (CCR) rates are a viable tool for characterizing the structural ensembles of intrinsically disordered proteins.
• Computational spectroscopy combined with MD simulations provides a powerful approach to calculate and interpret CCR rates for disordered systems.
• This study advances the understanding of IDP structure and dynamics, offering a new avenue for experimental data interpretation.