IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1011010.html
   My bibliography  Save this article

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Author

Listed:
  • Carlos A Gandarilla-Pérez
  • Sergio Pinilla
  • Anne-Florence Bitbol
  • Martin Weigt

Abstract

Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.Author summary: When two protein families interact, their sequences feature statistical dependencies. First, interacting proteins tend to share a common evolutionary history. Second, maintaining structure and interactions through the course of evolution yields coevolution, detectable via correlations in the amino-acid usage at contacting sites. Both signals can be used to computationally predict which proteins are specific interaction partners among the paralogs of two interacting protein families, starting just from their sequences. We show that combining them improves the performance of interaction partner inference, especially when the average number of potential partners is large and when the total data set size is modest. The resulting paired multiple-sequence alignments might be used as input to machine-learning algorithms to improve protein-complex structure prediction, as well as to understand interaction specificity in signaling pathways.

Suggested Citation

  • Carlos A Gandarilla-Pérez & Sergio Pinilla & Anne-Florence Bitbol & Martin Weigt, 2023. "Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins," PLOS Computational Biology, Public Library of Science, vol. 19(3), pages 1-19, March.
  • Handle: RePEc:plo:pcbi00:1011010
    DOI: 10.1371/journal.pcbi.1011010
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011010
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011010&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1011010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Anne-Florence Bitbol, 2018. "Inferring interaction partners from protein sequences using mutual information," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-24, November.
    4. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Author Correction: Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    5. Guillaume Marmier & Martin Weigt & Anne-Florence Bitbol, 2019. "Phylogenetic correlations can suffice to infer protein partners from sequences," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-24, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Genki Hibi & Taro Shiraishi & Tatsuki Umemura & Kenji Nemoto & Yusuke Ogura & Makoto Nishiyama & Tomohisa Kuzuyama, 2023. "Discovery of type II polyketide synthase-like enzymes for the biosynthesis of cispentacin," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Yuteng Weng & Yanhuizhi Feng & Zeyuan Li & Shuyu Xu & Di Wu & Jie Huang & Haicheng Wang & Zuolin Wang, 2024. "Zfp260 choreographs the early stage osteo-lineage commitment of skeletal stem cells," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    4. Patrick Bryant & Gabriele Pozzati & Wensi Zhu & Aditi Shenoy & Petras Kundrotas & Arne Elofsson, 2022. "Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Brooke M. Britton & Remy A. Yovanno & Sara F. Costa & Joshua McCausland & Albert Y. Lau & Jie Xiao & Zach Hensel, 2023. "Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Le Tracy Yu & Mark A. B. Kreutzberger & Thi H. Bui & Maria C. Hancu & Adam C. Farsheed & Edward H. Egelman & Jeffrey D. Hartgerink, 2024. "Exploration of the hierarchical assembly space of collagen-like peptides beyond the triple helix," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Patrick Bryant & Frank Noé, 2024. "Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile," PLOS Computational Biology, Public Library of Science, vol. 20(7), pages 1-12, July.
    8. Christoph Buhlheller & Theo Sagmeister & Christoph Grininger & Nina Gubensäk & Uwe B. Sleytr & Isabel Usón & Tea Pavkov-Keller, 2024. "SymProFold: Structural prediction of symmetrical biological assemblies," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Z. Faidon Brotzakis & Shengyu Zhang & Mhd Hussein Murtada & Michele Vendruscolo, 2025. "AlphaFold prediction of structural ensembles of disordered proteins," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    10. Tânia F. Custódio & Maxime Killer & Dingquan Yu & Virginia Puente & Daniel P. Teufel & Alexander Pautsch & Gisela Schnapp & Marc Grundl & Jan Kosinski & Christian Löw, 2023. "Molecular basis of TASL recruitment by the peptide/histidine transporter 1, PHT1," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    11. Devlina Chakravarty & Joseph W. Schafer & Ethan A. Chen & Joseph F. Thole & Leslie A. Ronish & Myeongsang Lee & Lauren L. Porter, 2024. "AlphaFold predictions of fold-switched conformations are driven by structure memorization," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    12. Marin Matic & Pasquale Miglionico & Manae Tatsumi & Asuka Inoue & Francesco Raimondi, 2023. "GPCRome-wide analysis of G-protein-coupling diversity using a computational biology approach," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    13. Kazutoshi Tani & Ryo Kanno & Xuan-Cheng Ji & Itsusei Satoh & Yuki Kobayashi & Malgorzata Hall & Long-Jiang Yu & Yukihiro Kimura & Akira Mizoguchi & Bruno M. Humbel & Michael T. Madigan & Zheng-Yu Wang, 2023. "Rhodobacter capsulatus forms a compact crescent-shaped LH1–RC photocomplex," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    14. Andreas Lackner & Michael Müller & Magdalena Gamperl & Delyana Stoeva & Olivia Langmann & Henrieta Papuchova & Elisabeth Roitinger & Gerhard Dürnberger & Richard Imre & Karl Mechtler & Paulina A. Lato, 2023. "The Fgf/Erf/NCoR1/2 repressive axis controls trophoblast cell fate," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    15. Jan Philipp Dobert & Jan-Hannes Schäfer & Thomas Dal Maso & Priyadarshini Ravindran & Dustin J. E. Huard & Eileen Socher & Lisa A. Schildmeyer & Raquel L. Lieberman & Wim Versées & Arne Moeller & Frie, 2025. "Cryo-TEM structure of β-glucocerebrosidase in complex with its transporter LIMP-2," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    16. Qianqiao Liu & Beth M. Stadtmueller, 2023. "SIgA structures bound to Streptococcus pyogenes M4 and human CD89 provide insights into host-pathogen interactions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. Yang Yue & Shu Li & Yihua Cheng & Lie Wang & Tingjun Hou & Zexuan Zhu & Shan He, 2024. "Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    18. Patrick Bryant & Frank Noé, 2024. "Structure prediction of alternative protein conformations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    19. Hélène Bret & Jinmei Gao & Diego Javier Zea & Jessica Andreani & Raphaël Guerois, 2024. "From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. Matthew C. Gaines & Michail N. Isupov & Mathew McLaren & Clara L. Mollat & Risat Ul Haque & Jake K. Stephenson & Shamphavi Sivabalasarma & Cyril Hanus & Daniel Kattnig & Vicki A. M. Gold & Sonja Alber, 2024. "Towards a molecular picture of the archaeal cell surface," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1011010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.