MCPath: Monte Carlo path generation approach to predict likely allosteric pathways and functional residues.

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Allosteric mechanisms are crucial for protein-mediated biomolecular signaling.
• Understanding communication pathways between functional residues is key to elucidating these mechanisms.

Purpose of the Study:

• To develop and implement a novel Monte Carlo (MC) path generation approach for defining likely allosteric pathways in proteins.
• To identify key residues involved in allosteric communication through an ensemble of maximum probability paths.

Main Methods:

• Protein structures modeled as residue networks with inter-residue interactions defined by an atomistic potential function.
• A Monte Carlo (MC) path generation algorithm used to create an ensemble of maximum probability paths.
• Case studies including PDZ domains, bovine rhodopsin, and myosin structures analyzed.

Main Results:

• The MC path generation approach successfully identified maximally probable allosteric pathways.
• The predicted pathways and constituent residues largely align with findings from previous studies.
• Demonstrated that protein communication pathways can be multiple and intrinsically present.

Conclusions:

• The MC path generation approach is an effective tool for predicting key residues that mediate allosteric communication.
• This method provides insights into the ensemble of pathways and functionally relevant residues in proteins.
• The MCPath server is publicly available for further research.