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Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences

Author

Listed:
  • Anna G. Green

    (Harvard Medical School)

  • Hadeer Elhabashy

    (Biomolecular Interactions, Max Planck Institute for Developmental Biology
    Institute for Bioinformatics and Medical Informatics, University of Tübingen
    University of Tübingen, WSI/ZBIT)

  • Kelly P. Brock

    (Harvard Medical School)

  • Rohan Maddamsetti

    (Harvard Medical School)

  • Oliver Kohlbacher

    (Biomolecular Interactions, Max Planck Institute for Developmental Biology
    Institute for Bioinformatics and Medical Informatics, University of Tübingen
    University of Tübingen, WSI/ZBIT
    Quantitative Biology Center, University of Tübingen)

  • Debora S. Marks

    (Institute for Bioinformatics and Medical Informatics, University of Tübingen
    Broad Institute of Harvard and MIT)

Abstract

Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.

Suggested Citation

  • Anna G. Green & Hadeer Elhabashy & Kelly P. Brock & Rohan Maddamsetti & Oliver Kohlbacher & Debora S. Marks, 2021. "Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21636-z
    DOI: 10.1038/s41467-021-21636-z
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    Citations

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    Cited by:

    1. Zhiye Guo & Jian Liu & Jeffrey Skolnick & Jianlin Cheng, 2022. "Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Lior Artzi & Assaf Alon & Kelly P. Brock & Anna G. Green & Amy Tam & Fernando H. Ramírez-Guadiana & Debora Marks & Andrew Kruse & David Z. Rudner, 2021. "Dormant spores sense amino acids through the B subunits of their germination receptors," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Lucien F. Krapp & Luciano A. Abriata & Fabio Cortés Rodriguez & Matteo Dal Peraro, 2023. "PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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