Identifying mechanistically distinct pathways in kinetic transition networks

Area of Science:

• Computational Chemistry
• Statistical Mechanics
• Biophysics

Background:

• Understanding complex dynamical processes requires identifying kinetically relevant pathways.
• Distinguishing between multiple competing mechanisms is crucial for analyzing molecular transitions.

Purpose of the Study:

• To implement a scalable path deviation algorithm for finding k most kinetically relevant paths in transition networks.
• To demonstrate the algorithm's ability to identify distinct pathways in systems with double-funnel energy landscapes.

Main Methods:

• Developed a scalable path deviation algorithm to analyze transition networks.
• Applied the algorithm to a 2D potential energy surface model ('three-hole' network) and the Lennard-Jones 38-atom cluster (LJ38).
• Analyzed path cost profiles and identified rate-limiting edges to distinguish pathway ensembles.

Main Results:

• The algorithm successfully identified distinct pathway ensembles for the 'three-hole' system, reflecting its complex landscape.
• For LJ38, the transition involved a single ensemble of pathways through disordered structures, as indicated by path cost profiles.
• The algorithm generates network cuts, analogous to transition dividing surfaces.

Conclusions:

• The algorithm is effective in identifying and assessing the kinetic relevance of distinguishable pathway ensembles.
• It provides insights into competing mechanisms in large stochastic network models and conformational transitions in biomolecules.
• This tool aids in understanding complex slow processes by revealing distinct dynamical pathways.