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PreprintCited by 35Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

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Pei, Jimin, Zhang, Jing, Cong, Qian·2021·Bioinformatics
DOI 10.1101/2021.09.14.460228

Preprint — published as 10.1093/bioinformatics/btac527 · Paper page

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Abstract

Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold2, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to the modeling of the complex structures by AlphaFold2, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most of the top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, ECSIT-NDUFAF1 and COQ7-COQ9, among others. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.

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Metadata

Authors
Pei, J., Zhang, J., & Cong, Q.
Affiliations
Harold C. Simmons Comprehensive Cancer Center (US)
Corresponding author
Cong, Qian
ORCID
Venue
Bioinformatics
Publisher
openRxiv
Published
2021-09-14
Volume
38
Issue
18
Pages
4301-4311
ISSN
1367-4803 (ISSN-L)
DOI
10.1101/2021.09.14.460228
OpenAlex
W4288039132
Cited by
35
References
1
Citations by year
2026: 2 · 2025: 5 · 2024: 16 · 2023: 9 · 2022: 3
FWCI
2.69
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Bioinformatics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Welch Foundation
  • Cancer Prevention and Research Institute of Texas (I-2095-20220331)
  • Southwestern Medical Foundation (RP210041)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
mitochondrial proteins, protein-protein interaction, protein complex structure prediction, coevolution, RoseTTAFold, AlphaFold, mitochondrial interactome, protein interface prediction, mitochondrial disease
MeSH terms
Deep Learning, Humans, Computational Biology, Proteome, Mitochondrial Proteins

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CoChat. (2026). Citation check: Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling. https://app.cochat.ai/papers/10.1101/2021.09.14.460228
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Pei, J., Zhang, J., & Cong, Q. (2021). Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling. Bioinformatics. https://doi.org/10.1101/2021.09.14.460228