Using Kinetic Network Models To Probe Non-Native Salt-Bridge Effects on α-Helix Folding

Area of Science:

• Biophysics
• Computational Biology
• Protein Science

Background:

• Salt-bridge interactions are crucial for protein structure stability and are key targets in protein design.
• Understanding how non-native interactions influence protein folding is essential for accurate structure prediction and design.

Purpose of the Study:

• To investigate the impact of non-native salt bridges on the folding kinetics of a soluble alanine-based peptide (Fs peptide).
• To develop methods for automatic detection and analysis of conformational states influenced by non-native salt bridges.

Main Methods:

• Extensive all-atom molecular dynamics simulations utilizing the Folding@home distributed computing platform.
• Application of Markov State Models (MSMs) to analyze simulation trajectories and identify key conformational states.
• Development of algorithms for automated detection and characterization of salt-bridge-perturbed states.

Main Results:

• Non-native salt bridges were shown to significantly perturb specific conformational states of the Fs peptide.
• These perturbations were demonstrated to alter the folding kinetics of the peptide.
• Novel methods for the automated detection and analysis of these critical conformational states were successfully developed.

Conclusions:

• Non-native salt bridges play a significant role in modulating protein folding pathways and kinetics.
• The findings provide valuable insights into helix folding mechanisms.
• This work offers practical guidance for enhancing simulation-based computational protein design strategies.