Emergence of power-law distributions in protein-protein interaction networks through study bias

Abstract
Data availability
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Abstract

Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study bias affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.

Data availability

We analyzed only previously published data for this work. To facilitate reproducibility, we deposited the used datasets at https://zenodo.org/record/8288898.

The following data sets were generated

(2023) Emergence of power law distributions in protein-protein interaction networks through study bias
Zenodo, https://doi.org/10.5281/zenodo.7695120.

https://zenodo.org/records/8288898

The following previously published data sets were used

(2014) intact.txt
IntAct Molecular Interaction Database, 10.1093/nar/gkt1115.

https://ftp.ebi.ac.uk/pub/databases/intact/2022-02-03/psimitab/
(2017) HIPPIE v2.2
HIPPIE Human Integrated Protein-Protein Interaction rEference.

https://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/download.php
1. UniProt Consortium
(2023) UniProt
UniProt, 10.1093/nar/gkac1052.

https://www.uniprot.org/uniprotkb?facets=model_organism\%3A9606&query=reviewed\%3Atrue
(2023) gene2pubmed
PubMed, 10.1093/nar/gkab1112.

https://ftp.ncbi.nih.gov/gene/DATA/
(2020) Table_S1_gene_info_at_protein_level.xlsx
GTEx, 10.1016/j.cell.2020.08.036.

https://gtexportal.org/home/downloads/egtex/proteomics

Article and author information

Author details

David B Blumenthal

Department Artificial Intelligence in Biomedical Engineering, University of Erlangen-Nuremberg, Erlangen, Germany

For correspondence
david.b.blumenthal@fau.de

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-8651-750X
Marta Lucchetta

Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

Competing interests
The authors declare that no competing interests exist.
Linda Kleist

Department of Computer Science, Technische Universität Braunschweig, Braunschweig, Germany

Competing interests
The authors declare that no competing interests exist.
Sándor P Fekete

Department of Computer Science, Technische Universität Braunschweig, Braunschweig, Germany

Competing interests
The authors declare that no competing interests exist.
Markus List

Data Science in Systems Biology, Technical University of Munich, Freising, Germany

For correspondence
markus.list@tum.de

Competing interests
The authors declare that no competing interests exist.
Martin H Schaefer

Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

For correspondence
martin.schaefer@ieo.it

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7503-6364

Funding

Bundesministerium für Bildung und Forschung (031L0309A)

David B Blumenthal

Klaus Tschira Stiftung (00.003.2024)

David B Blumenthal
Markus List
Martin H Schaefer

Fondazione AIRC per la ricerca sul cancro ETS (MFAG 21791)

Martin H Schaefer

Fondazione AIRC per la ricerca sul cancro ETS (Bridge Grant n. 29162)

Martin H Schaefer

Ministero della Salute (Ricerca Corrente)

Martin H Schaefer

Ministero della Salute (5x1000 funds)

Martin H Schaefer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.