Back

Protein-protein interaction is a major source of epistasis in genetic interaction networks

Castellanos-Girouard, X.; Serohijos, A. W.; Michnick, S. W.

2025-02-09 systems biology
10.1101/2024.11.11.623081 bioRxiv
Show abstract

Genetic interaction (GI) and protein-protein interaction (PPI) networks have proved among the most important tools for inference of gene function and genotype-phenotype relationship1-6. It remains unclear, however, how these two networks are related to each other. Here we demonstrate that the strengths of epistatic genetic interactions between two genes strongly correlate with the binding free energies of interactions between their cognate proteins, for both yeast and human genomes. Consequently, we show that GI and PPI networks can reciprocally predict each other. Further, we observe that functional divergence and redundancy in duplicated genes (paralogs) are reflected in the binding affinity of their interactions with other proteins and in their genetic interaction strengths. Finally, we demonstrate that the overall topologies of GI and PPI networks significantly overlap in two topological features: Modules, in which genes/proteins are organized by common function, and connectors, which link modules to each other. This opens avenues for new integrated network approaches in understanding cellular processes and flow of genomic information.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 2%
32.2%
Similar papers in this journal
2
Nature
○ 645 papers in training set
Top 1%
10.7%
Similar papers in this journal
3
Molecular Systems Biology
○ 162 papers in training set
Top 0.1%
8.6%
Similar papers in this journal
50% of probability mass above
4
Nature Structural & Molecular Biology
236 papers in training set
Top 0.9%
5.3%
5
Cell Systems
○ 201 papers in training set
Top 0.9%
5.0%
Similar papers in this journal
6
eLife
● 5828 papers in training set
Top 29%
4.2%
7
Nature Methods
○ 385 papers in training set
Top 3%
3.4%
8
Molecular Cell
○ 350 papers in training set
Top 2%
2.7%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 22%
2.4%
10
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.3%
11
Nature Microbiology
○ 155 papers in training set
Top 1%
2.3%
12
Nature Genetics
○ 286 papers in training set
Top 3%
2.1%
13
Communications Biology
○ 993 papers in training set
Top 15%
1.7%
14
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.7%
15
Cell Reports
○ 1498 papers in training set
Top 21%
1.4%
16
Genome Biology
○ 637 papers in training set
Top 8%
1.1%
17
Science Advances
● 1243 papers in training set
Top 28%
1.0%
18
Nature Chemical Biology
○ 119 papers in training set
Top 3%
1.0%
19
iScience
○ 1154 papers in training set
Top 33%
1.0%
20
PLOS Computational Biology
● 1863 papers in training set
Top 20%
0.8%
21
Cell
○ 431 papers in training set
Top 10%
0.8%
22
Nature Cell Biology
○ 118 papers in training set
Top 4%
0.6%
23
Nature Immunology
○ 79 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.