MinActionPath

MinActionPath computes the most probable transition path between two known macromolecular (protein) states under harmonic potentials to characterize structural transitions and associated energy profiles.

Key Features:

• Analytical Solution via Onsager-Machlup Formalism: Employs Onsager-Machlup action minimization to solve the Langevin equation analytically, converting the non-linear problem into a pair of linear differential equations linked by a non-linear boundary matching condition.
• Elastic Network Model (ENM) Utilization: Uses the classical Cα-based Elastic Network Model (ENM) for both initial and final states and permits integration of other ENM variants.
• Iterative Numerical Approach: Applies an iterative numerical method to determine the crossover between multidimensional energy curves around each state, identify the most probable trajectory, and characterize the transition state and associated energy profile.

Scientific Applications:

• Structural biology: Analysis of protein dynamics and conformational transitions between known structural states.
• Drug design: Elucidation of mechanisms of molecular function and interactions relevant to ligand binding.
• Enzyme engineering: Characterization of stability and conformational changes relevant to enzyme design.
• Protein folding pathways: Identification and characterization of transition states and energy profiles in folding or conformational change pathways.

Methodology:

Solves the Langevin equation under harmonic potential conditions using Onsager-Machlup action minimization, converts the problem into a pair of linear differential equations with a non-linear boundary matching condition, models initial and final states with a Cα-based ENM, and employs an iterative numerical method to determine crossovers between multidimensional energy curves and the most probable transition trajectory with its transition state and energy profile.