Markov-State Transition Path Analysis of Electrostatic Channeling.

Area of Science:

• Computational chemistry and biochemistry
• Enzyme catalysis and engineering
• Molecular dynamics and simulation

Background:

• Electrostatic channeling controls charged intermediates in catalytic cascades.
• Computational methods provide atomic-level understanding of molecular interactions.
• Artificial enzyme cascades offer platforms for studying reaction mechanisms.

Purpose of the Study:

• To apply Markov-state modeling (MSM) for the first time to describe surface diffusion of glucose 6-phosphate.
• To analyze intermediate transport on an artificially constructed enzyme cascade.
• To investigate the role of electrostatic interactions in controlling reaction pathways.

Main Methods:

• Development of a Markov-state model (MSM) to represent intermediate diffusion on enzyme surfaces.
• Utilized conformation space networks and committor probabilities to assess desorption.
• Integrated kinetic Monte Carlo (KMC) simulations with parameters from various computational techniques.

Main Results:

• MSM accurately described the surface diffusion of glucose 6-phosphate on a covalently linked enzyme cascade.
• Calculated desorption probabilities showed good agreement with transition state theory, particularly concerning ionic strength dependence.
• KMC simulations quantified the contribution of desorption at each step of the channeling pathway.

Conclusions:

• The study validates the use of MSM and advanced sampling techniques for analyzing complex reaction networks.
• Computational modeling, including MSM and KMC, can effectively guide the design of artificial catalytic systems.
• This work demonstrates a powerful approach for understanding and optimizing enzyme-mediated processes.