Rapid Proteome-Wide Discovery of Protein–Protein Interactions With ppIRIS
Piochi, Luiz Felipe ; Tang, Di LU
; Malmström, Johan
LU
; Karami, Yasaman
and Khakzad, Hamed
(2026)
In Advanced Science
- Abstract
- Protein–protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interactome mapping remains challenging at the proteome scale. Experimental approaches provide only partial coverage, while existing computational methods often lack generalizability across species or are too resource-intensive for large-scale screening. Here, we introduce ppIRIS (protein–protein Interaction Regression via Iterative Siamese networks), a lightweight deep learning framework that integrates evolutionary and structural embeddings to predict PPIs directly from sequence. Evaluated on multi-species benchmarks, ppIRIS achieves state-of-the-art accuracy while enabling proteome-wide screening... (More)
- Protein–protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interactome mapping remains challenging at the proteome scale. Experimental approaches provide only partial coverage, while existing computational methods often lack generalizability across species or are too resource-intensive for large-scale screening. Here, we introduce ppIRIS (protein–protein Interaction Regression via Iterative Siamese networks), a lightweight deep learning framework that integrates evolutionary and structural embeddings to predict PPIs directly from sequence. Evaluated on multi-species benchmarks, ppIRIS achieves state-of-the-art accuracy while enabling proteome-wide screening in minutes. Trained on curated bacterial datasets and applied to the Group A Streptococcus (GAS) proteome, ppIRIS identified functional clusters associated with virulence pathways, such as nutrient transport, stress response, and metal scavenging. Extending to cross-species prediction, ppIRIS recovered 56.2% of known GAS-human plasma interactions with enrichment in complement, coagulation, and protease inhibition pathways. Experimental validation confirmed novel predictions, demonstrating the applicability of ppIRIS for systematic discovery of bacterial and cross-species PPIs. The model together with a Google Colaboratory is freely available at github.com/lupiochi/ppIRIS. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2e5427a7-ae8c-4c19-9635-5c0b848dd6ed
- author
- Piochi, Luiz Felipe
; Tang, Di
LU
; Malmström, Johan
LU
; Karami, Yasaman
and Khakzad, Hamed
- organization
- publishing date
- 2026-03-12
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- Advanced Science
- article number
- e21310
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:41816949
- scopus:105032805670
- ISSN
- 2198-3844
- DOI
- 10.1002/advs.202521310
- project
- Properties of Protective Antibody Responses against Bacterial Pathogens
- language
- English
- LU publication?
- yes
- id
- 2e5427a7-ae8c-4c19-9635-5c0b848dd6ed
- date added to LUP
- 2026-03-12 14:25:19
- date last changed
- 2026-05-11 15:56:43
@article{2e5427a7-ae8c-4c19-9635-5c0b848dd6ed,
abstract = {{Protein–protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interactome mapping remains challenging at the proteome scale. Experimental approaches provide only partial coverage, while existing computational methods often lack generalizability across species or are too resource-intensive for large-scale screening. Here, we introduce ppIRIS (protein–protein Interaction Regression via Iterative Siamese networks), a lightweight deep learning framework that integrates evolutionary and structural embeddings to predict PPIs directly from sequence. Evaluated on multi-species benchmarks, ppIRIS achieves state-of-the-art accuracy while enabling proteome-wide screening in minutes. Trained on curated bacterial datasets and applied to the Group A Streptococcus (GAS) proteome, ppIRIS identified functional clusters associated with virulence pathways, such as nutrient transport, stress response, and metal scavenging. Extending to cross-species prediction, ppIRIS recovered 56.2% of known GAS-human plasma interactions with enrichment in complement, coagulation, and protease inhibition pathways. Experimental validation confirmed novel predictions, demonstrating the applicability of ppIRIS for systematic discovery of bacterial and cross-species PPIs. The model together with a Google Colaboratory is freely available at github.com/lupiochi/ppIRIS.}},
author = {{Piochi, Luiz Felipe and Tang, Di and Malmström, Johan and Karami, Yasaman and Khakzad, Hamed}},
issn = {{2198-3844}},
language = {{eng}},
month = {{03}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Advanced Science}},
title = {{Rapid Proteome-Wide Discovery of Protein–Protein Interactions With ppIRIS}},
url = {{http://dx.doi.org/10.1002/advs.202521310}},
doi = {{10.1002/advs.202521310}},
year = {{2026}},
}