Conformational-relaxation models of single-enzyme kinetics

Area of Science:

• Biophysics
• Enzyme kinetics
• Computational chemistry

Background:

• Single-molecule fluorescent spectroscopy generates extensive data on enzyme dynamics.
• Stochastic models are crucial for interpreting this complex statistical data.
• Enzyme turnover involves a sequence of conformational changes.

Purpose of the Study:

• To develop and compare two stochastic models of enzyme action based on conformational transformations.
• To investigate the impact of different catalytic pathway structures on enzyme behavior.
• To assess the models' ability to replicate experimental single-molecule data.

Main Methods:

• Development of two stochastic models: a single-path and a double-path enzyme turnover model.
• Numerical simulations to analyze molecular statistics, memory landscapes, and cycle time distributions.
• Comparison of model outputs with experimental data, specifically for horseradish peroxidase.

Main Results:

• Both models produce non-Markovian molecular statistics, indicating memory effects.
• The two models exhibit significantly different memory landscapes and cycle time distributions.
• The double-path model's memory landscape shows strong agreement with experimental data for horseradish peroxidase.

Conclusions:

• Enzyme conformational dynamics can be effectively modeled using stochastic approaches.
• The presence of multiple catalytic pathways significantly alters enzyme statistics.
• The double-path model provides a more accurate representation of horseradish peroxidase single-molecule behavior.