MSMPathfinder: Identification of Pathways in Markov State Models

Area of Science:

• Computational Chemistry
• Biophysics
• Chemical Kinetics

Background:

• Markov state models (MSMs) interpret biomolecular processes via transitions between conformational states.
• Current methods excel at identifying frequent pathways but struggle with low-probability pathways that influence overall flux.
• Understanding the full spectrum of pathways is crucial for mechanistic insights.

Purpose of the Study:

• To develop a method for systematically characterizing the multitude of unique pathways in complex systems.
• To quantitatively calculate the correct weights and associated waiting times for all pathways with predefined accuracy.
• To analyze the contribution of diverse pathways to overall processes like protein folding.

Main Methods:

• Systematic construction of all possible pathways.
• Development of the MSMPathfinder method.
• Application to chiral transitions of peptide helices and villin headpiece folding.
• Kinetic network representation for analysis.

Main Results:

• MSMPathfinder can characterize up to 10^10 unique pathways in complex systems.
• The method quantitatively calculates pathway weights and waiting times accurately.
• Analysis of peptide helix and villin headpiece folding revealed insights into their mechanisms.
• Waiting time distributions may not fully represent pathway diversity.

Conclusions:

• The developed method provides a comprehensive approach to analyzing complex reaction pathways.
• Protein folding times are influenced by the cumulative effect of numerous pathways, not solely the most probable ones.
• This work offers a more complete understanding of biomolecular mechanisms by considering all pathways.