AllohubPy: Detecting Allosteric Signals Through An Information-theoretic Approach

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Allosteric regulation is vital for biological processes but not fully understood.
• Existing methods like AlloHubMat and GSAtools use information theory and graph analysis.
• These methods simplify complex molecular dynamics data using Structural Alphabets.

Purpose of the Study:

• To present AllohubPy, an improved and standardized Python-based methodology for studying allosteric regulation.
• To analyze AllohubPy's performance, limitations, and sampling requirements.
• To expand the methodology with new Structural Alphabets and Protein Language Models.

Main Methods:

• Developed AllohubPy, a Python package based on AlloHubMat and GSAtools.
• Utilized extensive molecular dynamics simulations of allosteric systems.
• Applied convergence analysis, diverse Structural Alphabets (e.g., 3DI), and Protein Language Models.

Main Results:

• AllohubPy demonstrates improved reliability and ease of use for capturing dynamic allosteric motions.
• The study analyzed performance, limitations, and sampling needs of the new tool.
• Integration with PLMs refined allosteric hub communication detection via evolutionary constraints.

Conclusions:

• AllohubPy enhances the study of allosteric regulation, offering a more accessible and reliable method.
• The tool effectively captures dynamic allosteric motions and residue pathways.
• AllohubPy is available as an open-source package on GitHub.