AI-based structural modelling and pathogenic variant mapping across the human protein interactome
- 1University of Waterloo
Abstract
Spanning the complex landscape of the human proteome, physical interactions between proteins coordinate cellular organization, signaling, regulation, and homeostasis. Yet the three-dimensional structures of most of these interactions remain unresolved. Here, we performed AI-based modelling of 109,282 human protein interaction structures, generating a structural resource for large-scale analysis. The predicted structures were systematically characterized through analysis of interface residue involvement, buried surface area, intermolecular bonding, and energetic properties. Additionally, we mapped pathogenic variant data onto the predicted structures, identifying interface-localized mutations for over 4,000 annotated disease categories. Acute myeloid leukemia, Alzheimer's disease, and cardiomyopathy were examined as representative disease contexts for exploring the distribution of interface mutations within disease-associated protein interaction networks. Strengths and limitations of predictive capabilities were also assessed, including through experimental structure comparisons. Collectively, this structural resource and accompanying analyses offer a basis for developing novel hypotheses and informing experimental studies involving human protein interactions in physiological and disease contexts.
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BibTeX
@misc{ille2026aibased,
title = {{AI-based structural modelling and pathogenic variant mapping across the human protein interactome}},
author = {A. M. Ille and A. L. Ille and H. H. Lamont and T. Holyoak},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.762585},
url = {https://www.biorxiv.org/content/10.64898/2026.10.28.762585v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - AI-based structural modelling and pathogenic variant mapping across the human protein interactome
AU - A. M. Ille
AU - A. L. Ille
AU - H. H. Lamont
AU - T. Holyoak
PY - 2026
DA - 2026/10/28
PB - bioRxiv
DO - 10.64898/2026.10.28.762585
UR - https://www.biorxiv.org/content/10.64898/2026.10.28.762585v1
AB - Spanning the complex landscape of the human proteome, physical interactions between proteins coordinate cellular organization, signaling, regulation, and homeostasis. Yet the three-dimensional structures of most of these interactions remain unresolved. Here, we performed AI-based modelling of 109,282 human protein interaction structures, generating a structural resource for large-scale analysis. The predicted structures were systematically characterized through analysis of interface residue involvement, buried surface area, intermolecular bonding, and energetic properties. Additionally, we mapped pathogenic variant data onto the predicted structures, identifying interface-localized mutations for over 4,000 annotated disease categories. Acute myeloid leukemia, Alzheimer's disease, and cardiomyopathy were examined as representative disease contexts for exploring the distribution of interface mutations within disease-associated protein interaction networks. Strengths and limitations of predictive capabilities were also assessed, including through experimental structure comparisons. Collectively, this structural resource and accompanying analyses offer a basis for developing novel hypotheses and informing experimental studies involving human protein interactions in physiological and disease contexts.
ER -