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PreprintCited by 31Bioinformatics and Genomic NetworksProtein Structure and DynamicsCancer Genomics and Diagnostics

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Computed cancer interactome explains the effects of somatic mutations in cancers

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Zhang, Jing, Pei, Jimin, Durham, Jesse, Bos, Tasia, Cong, Qian·2022·Protein Science
DOI 10.1101/2022.01.21.477304PMC PMC9667826

Preprint — published as 10.1002/pro.4479 · Paper page

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Abstract

Protein-protein interactions (PPIs) are involved in almost all essential cellular processes. Perturbation of PPI networks plays critical roles in tumorigenesis, cancer progression and metastasis. While numerous high-throughput experiments have produced a vast amount of data for PPIs, these datasets suffer from high false positive rates and exhibit a high degree of discrepancy. Coevolution of amino acid positions between protein pairs has proven to be useful in identifying interacting proteins and providing structural details of the interaction interfaces with the help of deep learning methods like AlphaFold (AF). In this study, we applied AF to investigate the cancer protein-protein interactome. We predicted 1,798 PPIs for cancer driver proteins involved in diverse cellular processes such as transcription regulation, signal transduction, DNA repair and cell cycle. We modeled the spatial structure for the predicted binary protein complexes, 1,087 of which lacked previous 3D structure information. Our predictions offer novel structural insight into many cancer-related processes such as the MAP kinase cascade and Fanconi anemia pathway. We further investigated the cancer mutation landscape by mapping somatic missense mutations (SMMs) in cancer to the predicted PPI interfaces and performing enrichment and depletion analyses. Interfaces enriched or depleted with SMMs exhibit different preferences for functional categories. Interfaces enriched in mutations tend to function in pathways that are deregulated in cancers and they may help explain the molecular mechanisms of cancers in patients; interfaces lacking mutations appear to be essential for the survival of cancer cells and thus may be future targets for PPI modulating drugs.

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Metadata

Authors
Zhang, J., Pei, J., Durham, J., Bos, T., & Cong, Q.
Affiliations
The University of Texas Southwestern Medical Center (US)
Corresponding author
Cong, Qian
ORCID
Venue
Protein Science
Publisher
openRxiv
Published
2022-01-23
Volume
31
Issue
12
Pages
e4479-e4479
ISSN
0961-8368 (ISSN-L)
DOI
10.1101/2022.01.21.477304
PMC
PMC9667826
OpenAlex
W4306914612
Cited by
31
References
1
Citations by year
2026: 4 · 2025: 10 · 2024: 12 · 2023: 3 · 2022: 2
FWCI
2.20
Type
journal article
Language
en
Open access
Locations
  • Protein Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Welch Foundation (I-2095-20220331)
  • Cancer Prevention and Research Institute of Texas (RP210041)
  • National Institute of General Medical Sciences (T32GM008297, T32GM131963)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
protein-protein interaction, somatic missense mutations, coevolution, AlphaFold, protein complex structure prediction, MAP kinase cascade, Fanconi anemia pathway
MeSH terms
Humans, Mutation, Neoplasms, Proteins, Mutation, Missense, Protein Interaction Mapping

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