Predicting Differential Protein-Protein Interactions
- 1Saarland University
Abstract
Motivation Analysis of context-specific protein-protein interactions (PPIs) provides deeper insight into protein function, revealing how cellular pathway components interact in certain perturbation conditions. However, identifying such interactions experimentally often requires combining affinity purification with mass spectrometry, proving costly and time-consuming. On the other hand, it has been shown that an increase in protein abundance does not always correspond to an increase in protein interaction, making it an unsuitable surrogate for interaction studies. Results To address this, a random forest model was created using PPI-specific MINT embeddings and condition-specific mass spectrometry protein abundance information to predict differential interactions across two experimental conditions. The model was trained and tested on validated context-specific PPIs from eight distinct cell-lines, demonstrating proficiency in predicting differential PPIs for both novel proteins and cell lines. To our knowledge, we provide the first computational model that predicts differential PPIs between experimental conditions. Availability and implementation The code is available on GitHub and backed up on Zenodo (DOI: 10.5281/zenodo.23758127). The model is freely available on Zenodo (DOI: 10.5281/zenodo.23757927).
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BibTeX
@misc{papazian2026predicting,
title = {{Predicting Differential Protein-Protein Interactions}},
author = {A. Papazian and V. Helms},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.762715},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.762715v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Predicting Differential Protein-Protein Interactions
AU - A. Papazian
AU - V. Helms
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.762715
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.762715v1
AB - Motivation Analysis of context-specific protein-protein interactions (PPIs) provides deeper insight into protein function, revealing how cellular pathway components interact in certain perturbation conditions. However, identifying such interactions experimentally often requires combining affinity purification with mass spectrometry, proving costly and time-consuming. On the other hand, it has been shown that an increase in protein abundance does not always correspond to an increase in protein interaction, making it an unsuitable surrogate for interaction studies. Results To address this, a random forest model was created using PPI-specific MINT embeddings and condition-specific mass spectrometry protein abundance information to predict differential interactions across two experimental conditions. The model was trained and tested on validated context-specific PPIs from eight distinct cell-lines, demonstrating proficiency in predicting differential PPIs for both novel proteins and cell lines. To our knowledge, we provide the first computational model that predicts differential PPIs between experimental conditions. Availability and implementation The code is available on GitHub and backed up on Zenodo (DOI: 10.5281/zenodo.23758127). The model is freely available on Zenodo (DOI: 10.5281/zenodo.23757927).
ER -