Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers

Area of Science:

• Biophysics
• Computational Biology
• Protein Dynamics

Background:

• Protein function is dictated by its free-energy surface, influencing thermodynamic and kinetic properties.
• Controlling protein conformational transition rates is crucial for protein engineering.
• Existing methods for analyzing free-energy landscapes can be computationally intensive.

Purpose of the Study:

• To propose a novel framework for reshaping protein free-energy landscapes.
• To control and engineer protein conformational transition rates.
• To validate the framework on point mutations of the miniprotein Chignolin.

Main Methods:

• Utilizing the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework.
• Constructing Harmonic Linear Discriminant Analysis (HLDA)-based collective variables (CVs) from short molecular dynamics trajectories.
• Confining simulations to metastable basins for limited sampling.

Main Results:

• The HLDA CV derived from wild-type data predicts mutation-induced changes in transition rates.
• Residue-level scores identify positions likely to accelerate or decelerate unfolding.
• The leading HLDA eigenvalue correlates significantly with transition rates across mutations.

Conclusions:

• Kinetic effects of point mutations can be inferred using minimal local sampling.
• The framework offers a practical approach to engineer protein transition rates.
• This method bypasses the need for exhaustive simulations or large datasets.