Predicting the order in which contacts are broken during single molecule protein stretching experiments

Area of Science:

• Computational biophysics
• Single-molecule biophysics
• Protein mechanics

Background:

• Understanding protein mechanical unfolding is crucial for deciphering biomolecular function.
• Gō-like and elastic network models are established computational tools for protein behavior analysis.
• Experimental techniques like atomic force microscopy probe protein responses to external forces.

Purpose of the Study:

• To develop and validate a combined computational approach for predicting the order of contact breakage in proteins under stretching.
• To correlate mechanical properties derived from elastic network models with unfolding pathways predicted by Gō-like models.
• To provide a predictive tool for interpreting single-molecule stretching experiments.

Main Methods:

• Stochastic dynamics simulations using a Gō-like model to simulate protein stretching and identify unfolding pathways.
• Elastic network model calculations to determine residue fluctuations (B-factors) and slowest modes of motion in stretched proteins.
• Comparison of predicted contact breakage from the elastic network model with Gō-like model unfolding scenarios.

Main Results:

• A Gaussian network model successfully predicts the sequence of contact breakage during protein stretching.
• Contact disruption is strongly correlated with the highest forces exerted by the protein backbone on specific residues.
• The computational prediction of bond-breaking aligns well with unfolding pathways observed in Gō-like simulations.

Conclusions:

• The integration of Gō-like and elastic network models offers a robust method for predicting mechanical unfolding sequences.
• This combined approach enhances the interpretation of single-molecule stretching experiments.
• The findings provide a valuable new tool for biophysicists studying protein mechanics.