Directional information flow as a tool for analyzing protein allostery
- 1Johns Hopkins University School of Medicine
Abstract
The ability to tune protein function through the binding of modulatory ligands enables the development of therapeutics that steer a biological system away from dysfunctional states underlying disease. Understanding the dynamic mechanisms by which allosteric ligands alter protein function remains an important open question. Dynamical network models allow us to quantify information flow between protein functional sites. However, existing network models use time-symmetric metrics for computing information from correlated residue motions extracted from molecular dynamics (MD) simulations, failing to fully capture directional information flow between sites. Here, we developed a Python library, TEntroPy, and analysis workflow using transfer entropy to generate a directional protein network from equilibrium MD trajectories. Applying this workflow to proteins with known allosteric ligands, we identified residues in both allosteric and orthosteric (primary) binding sites acting as broadcasters and receivers of information. We then computed optimal paths of directional information flow between binding sites. The presence of temporal asymmetry in residue coupling identified from simulations of the unbound (apo) state suggests that directional information flow is encoded in the intrinsic dynamics of the protein. To test this, we perturbed key binding-site residues and demonstrated that our TE-weighted network captures perturbation-induced changes in dynamics along communication routes between binding sites. Our work highlights transfer entropy as an additional tool for understanding the changes in flexibility and rigidity that drive dynamic mechanisms of allosteric communication.
Related
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- AlloPool is a deep learning framework that infers protein allostery from molecular dynamics simulations
- Efficient structural information integration into protein language models with implicit mixture-of-experts ProBEiT
BibTeX
@misc{yovanno2026directional,
title = {{Directional information flow as a tool for analyzing protein allostery}},
author = {R. A. Yovanno and A. Y. Lau},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.22.733418},
url = {https://www.biorxiv.org/content/10.64898/2026.06.22.733418v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Directional information flow as a tool for analyzing protein allostery
AU - R. A. Yovanno
AU - A. Y. Lau
PY - 2026
DA - 2026/06/22
PB - bioRxiv
DO - 10.64898/2026.06.22.733418
UR - https://www.biorxiv.org/content/10.64898/2026.06.22.733418v2
AB - The ability to tune protein function through the binding of modulatory ligands enables the development of therapeutics that steer a biological system away from dysfunctional states underlying disease. Understanding the dynamic mechanisms by which allosteric ligands alter protein function remains an important open question. Dynamical network models allow us to quantify information flow between protein functional sites. However, existing network models use time-symmetric metrics for computing information from correlated residue motions extracted from molecular dynamics (MD) simulations, failing to fully capture directional information flow between sites. Here, we developed a Python library, TEntroPy, and analysis workflow using transfer entropy to generate a directional protein network from equilibrium MD trajectories. Applying this workflow to proteins with known allosteric ligands, we identified residues in both allosteric and orthosteric (primary) binding sites acting as broadcasters and receivers of information. We then computed optimal paths of directional information flow between binding sites. The presence of temporal asymmetry in residue coupling identified from simulations of the unbound (apo) state suggests that directional information flow is encoded in the intrinsic dynamics of the protein. To test this, we perturbed key binding-site residues and demonstrated that our TE-weighted network captures perturbation-induced changes in dynamics along communication routes between binding sites. Our work highlights transfer entropy as an additional tool for understanding the changes in flexibility and rigidity that drive dynamic mechanisms of allosteric communication.
ER -