Reciprocal effective conductance-based inference of protein-protein relationships delineates protein network hubs and macromolecular properties
- 1University of Illinois Urbana-Champaign
Abstract
Protein-protein interactions (PPIs) are fundamental to the regulation and function of all cellular processes, yet our current understanding of the human interactome is limited by the specific PPI-related experiments performed to date. Moreover, whereas proteins with minimal research interest may have relatively few interaction-probing studies, other factors such as cancer related proteins are frequently represented in PPI studies, producing a highly uneven bias across current PPI data. Here, we apply effective conductance - a concept from circuit theory - to infer proximity relationships across all individual protein pairs on the basis of 1.2 million reported human PPI observations. Using a reciprocal analysis framework built on effective conductance, we identify statistically meaningful interactions within the network while otherwise filtering nonrelevant associations that are abundant in proteins with strong popularity bias. While this approach helps clarify the structural and functional properties of both minimally and maximally connected proteins, we further compute an interaction index between all macromolecular, multi-subunit complexes based on underlying protein-protein conductance scores. Leveraging this framework, which we name CoBRAA (Conductance-Based Reciprocal Association Analysis), we highlight unexpected findings, such as evidence that DNA-dependent RNA polymerase I, II, and III are characterized by surprisingly pervasive cross-complex interactions between enzyme-specific subunits, suggesting a potential for crosstalk between all three RNA polymerases. We further introduce CoBRAA as an open-access research tool that enables custom, user-specified exploration of both protein- and macromolecular-level interactions. We expect this resource to facilitate broader investigation and hypothesis generation across a wide range of proteins, pathways, and macromolecular systems.
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BibTeX
@misc{k2026reciprocal,
title = {{Reciprocal effective conductance-based inference of protein-protein relationships delineates protein network hubs and macromolecular properties}},
author = {R. K C and S. Zhou and R. Cheng and M. A. Penaherrera and S. Lizarazo and K. Van Bortle},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758675},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758675v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Reciprocal effective conductance-based inference of protein-protein relationships delineates protein network hubs and macromolecular properties
AU - R. K C
AU - S. Zhou
AU - R. Cheng
AU - M. A. Penaherrera
AU - S. Lizarazo
AU - K. Van Bortle
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758675
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758675v1
AB - Protein-protein interactions (PPIs) are fundamental to the regulation and function of all cellular processes, yet our current understanding of the human interactome is limited by the specific PPI-related experiments performed to date. Moreover, whereas proteins with minimal research interest may have relatively few interaction-probing studies, other factors such as cancer related proteins are frequently represented in PPI studies, producing a highly uneven bias across current PPI data. Here, we apply effective conductance - a concept from circuit theory - to infer proximity relationships across all individual protein pairs on the basis of 1.2 million reported human PPI observations. Using a reciprocal analysis framework built on effective conductance, we identify statistically meaningful interactions within the network while otherwise filtering nonrelevant associations that are abundant in proteins with strong popularity bias. While this approach helps clarify the structural and functional properties of both minimally and maximally connected proteins, we further compute an interaction index between all macromolecular, multi-subunit complexes based on underlying protein-protein conductance scores. Leveraging this framework, which we name CoBRAA (Conductance-Based Reciprocal Association Analysis), we highlight unexpected findings, such as evidence that DNA-dependent RNA polymerase I, II, and III are characterized by surprisingly pervasive cross-complex interactions between enzyme-specific subunits, suggesting a potential for crosstalk between all three RNA polymerases. We further introduce CoBRAA as an open-access research tool that enables custom, user-specified exploration of both protein- and macromolecular-level interactions. We expect this resource to facilitate broader investigation and hypothesis generation across a wide range of proteins, pathways, and macromolecular systems.
ER -