Predicting order of conformational changes during protein conformational transitions using an interpolated elastic...

Area of Science:

• Structural Biology and Biophysics
• Computational Biology and Bioinformatics

Background:

• Understanding protein conformational transitions is crucial for elucidating the molecular mechanisms of biological nanomachines.
• Coarse-grained models offer efficient simulations of protein dynamics, aiding in the study of these complex transitions.

Purpose of the Study:

• To develop a novel interpolated elastic network model for generating protein conformational transition pathways.
• To validate the model by predicting the sequence of structural events in key ATP-driven transitions of biological nanomachines.

Main Methods:

• Combined two coarse-grained elastic network models representing initial and final protein conformations.
• Developed an interpolated elastic network model to generate a transition pathway.
• Applied the model to predict conformational changes in myosin, F(1) ATPase, and chaperonin GroEL.

Main Results:

• The interpolated elastic network model successfully generated transition pathways for ATP-driven protein conformational changes.
• Predicted that local conformational changes at the active site precede global mechanical motions during these transitions.
• Findings align with experimental data on intermediate protein structures.

Conclusions:

• The developed interpolated elastic network model is effective for studying protein conformational dynamics.
• Local active site dynamics play a critical role in initiating or regulating conformational transitions in biological nanomachines.
• This approach provides insights into the functional mechanisms of diverse molecular machines.