Constructing a folding model for protein S6 guided by native fluctuations deduced from NMR structures.

Area of Science:

• Biophysics
• Structural Biology
• Computational Biology

Background:

• Protein dynamics are crucial for native state function and are governed by the energy landscape guiding folding.
• Structure-based models (SBMs) utilize energy landscape theory and native structure information to model protein folding.
• Native state fluctuations can be estimated from the diversity within nuclear magnetic resonance (NMR) structural ensembles.

Purpose of the Study:

• To refine structure-based protein models (SBMs) by incorporating native state dynamics derived from NMR structural diversity.
• To investigate the connection between protein folding, native state dynamics, and function through improved SBMs.
• To demonstrate that accounting for native state dynamics enhances the prediction of protein folding mechanisms.

Main Methods:

• Estimating native state fluctuations directly from the diversity in NMR structures.
• Modifying contact maps in SBMs to incorporate inferred protein dynamics.
• Utilizing energy landscape theory and the principle of minimal frustration in SBM construction.

Main Results:

• A highly flexible loop in ribosomal protein S6 was identified using NMR structural diversity.
• Modifications to the SBM's contact map based on inferred dynamics improved folding predictions.
• The model successfully recovered the experimental transition state and folding order when native dynamics were included.

Conclusions:

• The diversity in NMR structures provides valuable insights into native state dynamics.
• Integrating native state dynamics into SBMs significantly improves the accuracy of predicting protein folding mechanisms.
• A comprehensive understanding of the native basin's energy landscape is key to connecting protein folding and function.