MCPath

MCPath predicts allosteric pathways and identifies functional residues that mediate inter-residue communication in proteins using Monte Carlo path generation guided by an atomistic potential function.

Key Features:

• Monte Carlo Path Generation: Employs a probabilistic Monte Carlo approach to generate an ensemble of maximum-probability paths representing potential communication routes between residues.
• Network-Based Protein Modeling: Represents protein structures as networks of amino acid residues to model inter-residue communication.
• Atomistic Potential Function: Quantifies inter-residue interactions using an atomistic potential function to guide path selection and probabilities.
• Multiple Pathway Prediction: Produces multiple probable communication pathways to reflect the intrinsic variability of allosteric signaling.
• Functional Residue Identification: Analyzes ensembles of paths to identify key residues likely to mediate allosteric communication.
• Case Studies and Validation: Validated with case studies on PDZ domain structures, bovine rhodopsin, and three myosin structures.

Scientific Applications:

• Protein dynamics and signaling: Elucidates allosteric communication routes relevant to biomolecular signaling and regulation.
• Functional residue mapping: Identifies candidate residues for experimental mutagenesis or functional annotation.
• Allosteric drug design: Supports identification of allosteric sites and pathways that could be targeted to modulate protein activity.

Methodology:

Proteins are modeled as networks of amino acid residues; a Monte Carlo simulation generates an ensemble of maximum-probability paths between functional residues guided by an atomistic potential function, and the resulting ensemble is analyzed to identify likely functional residues and multiple probable pathways.