Related Experiment Video
Updated: Jul 4, 2026
07:08
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Directional information flow as a tool for analyzing protein allostery
Remy A Yovanno1, Albert Y Lau1,2
1Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Biorxiv : the Preprint Server for Biology
|July 3, 2026
Summary
This study introduces TEntroPy, a Python library for analyzing directional information flow in proteins using transfer entropy. It reveals how allosteric ligands modulate protein dynamics and function.
Area of Science:
- Biophysics
- Computational Biology
- Structural Biology
Background:
- Allosteric ligands can tune protein function, crucial for developing disease therapeutics.
- Understanding the dynamic mechanisms of allosteric ligand action is essential.
- Existing network models lack directional information flow analysis.
Purpose of the Study:
- Develop a method to quantify directional information flow in proteins.
- Analyze allosteric communication pathways using transfer entropy.
- Investigate the role of intrinsic protein dynamics in allosteric regulation.
Main Methods:
- Developed the Python library TEntroPy.
- Applied transfer entropy to molecular dynamics (MD) simulations.
- Generated directional protein networks and computed optimal information flow paths.
Main Results:
- Identified key residues acting as information broadcasters and receivers in binding sites.
- Demonstrated that directional information flow is encoded in intrinsic protein dynamics.
- Showed that the TE-weighted network captures perturbation-induced dynamic changes.
Conclusions:
- TEntroPy provides a novel tool for analyzing allosteric communication.
- Temporal asymmetry in residue coupling reveals directional information flow.
- This approach enhances understanding of dynamic mechanisms in allosteric regulation.
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