IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-59175-6.html
   My bibliography  Save this article

EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals

Author

Listed:
  • Aidan H. Lakshman

    (University of Pittsburgh)

  • Erik S. Wright

    (University of Pittsburgh
    Center for Evolutionary Biology and Medicine)

Abstract

The known universe of uncharacterized proteins is expanding far faster than our ability to annotate their functions through laboratory study. Computational annotation approaches rely on similarity to previously studied proteins, thereby ignoring unstudied proteins. Coevolutionary approaches hold promise for injecting new information into our knowledge of the protein universe by linking proteins through ‘guilt-by-association’. However, existing coevolutionary algorithms have insufficient accuracy and scalability to connect the entire universe of proteins. We present EvoWeaver, a method that weaves together 12 signals of coevolution to quantify the degree of shared evolution between genes. EvoWeaver accurately identifies proteins involved in protein complexes or separate steps of a biochemical pathway. We show the merits of EvoWeaver by partly reconstructing known biochemical pathways without any prior knowledge other than that available from genomic sequences. Applying EvoWeaver to 1545 gene groups from 8564 genomes reveals missing connections in popular databases and potentially undiscovered links between proteins.

Suggested Citation

  • Aidan H. Lakshman & Erik S. Wright, 2025. "EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59175-6
    DOI: 10.1038/s41467-025-59175-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-59175-6
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-59175-6?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. 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.
    2. Doron Stupp & Elad Sharon & Idit Bloch & Marinka Zitnik & Or Zuk & Yuval Tabach, 2021. "Co-evolution based machine-learning for predicting functional interactions between human genes," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. repec:plo:pbio00:2006643 is not listed on IDEAS
    4. Jesse Gillis & Paul Pavlidis, 2011. "The Impact of Multifunctional Genes on "Guilt by Association" Analysis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-16, February.
    5. Yuval Tabach & Allison C. Billi & Gabriel D. Hayes & Martin A. Newman & Or Zuk & Harrison Gabel & Ravi Kamath & Keren Yacoby & Brad Chapman & Susana M. Garcia & Mark Borowsky & John K. Kim & Gary Ruvk, 2013. "Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence," Nature, Nature, vol. 493(7434), pages 694-698, January.
    6. Aidan H. Lakshman & Erik S. Wright, 2025. "EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    7. Katja Luck & Dae-Kyum Kim & Luke Lambourne & Kerstin Spirohn & Bridget E. Begg & Wenting Bian & Ruth Brignall & Tiziana Cafarelli & Francisco J. Campos-Laborie & Benoit Charloteaux & Dongsic Choi & At, 2020. "A reference map of the human binary protein interactome," Nature, Nature, vol. 580(7803), pages 402-408, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aidan H. Lakshman & Erik S. Wright, 2025. "EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals," Nature Communications, Nature, vol. 16(1), pages 1-16, December.

    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. Manjit Kumar Srivastav & H. Diego Folco & Patroula Nathanailidou & Anupa T Anil & Drisya Vijayakumari & Shweta Jain & Jothy Dhakshnamoorthy & Maura O’Neill & Thorkell Andresson & David Wheeler & Shiv , 2025. "PhpCNF-Y transcription factor infiltrates heterochromatin to generate cryptic intron-containing transcripts crucial for small RNA production," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
    2. 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.
    3. Meriem Mekedem & Patrice Ravel & Jacques Colinge, 2022. "Application of modular response analysis to medium- to large-size biological systems," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-18, April.
    4. Nilesh Kumar & M. Shahid Mukhtar, 2024. "Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study," Data, MDPI, vol. 9(8), pages 1-12, August.
    5. Ghulam Muhiuddin & Sovan Samanta & Abdulrahman F. Aljohani & Abeer M. Alkhaibari, 2023. "A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing," Mathematics, MDPI, vol. 11(14), pages 1-18, July.
    6. Diego Esposito & Jane Dudley-Fraser & Acely Garza-Garcia & Katrin Rittinger, 2022. "Divergent self-association properties of paralogous proteins TRIM2 and TRIM3 regulate their E3 ligase activity," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Bingjie Hao & István A. Kovács, 2023. "A positive statistical benchmark to assess network agreement," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. Maik Müller & Fabienne Gräbnitz & Niculò Barandun & Yang Shen & Fabian Wendt & Sebastian N. Steiner & Yannik Severin & Stefan U. Vetterli & Milon Mondal & James R. Prudent & Raphael Hofmann & Marc Oos, 2021. "Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    9. Pisanu Buphamalai & Tomislav Kokotovic & Vanja Nagy & Jörg Menche, 2021. "Network analysis reveals rare disease signatures across multiple levels of biological organization," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    10. Xu-Wen Wang & Lorenzo Madeddu & Kerstin Spirohn & Leonardo Martini & Adriano Fazzone & Luca Becchetti & Thomas P. Wytock & István A. Kovács & Olivér M. Balogh & Bettina Benczik & Mátyás Pétervári & Be, 2023. "Assessment of community efforts to advance network-based prediction of protein–protein interactions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    11. Jens S. Andersen & Aaran Vijayakumaran & Christopher Godbehere & Esben Lorentzen & Vito Mennella & Kenneth Bødtker Schou, 2024. "Uncovering structural themes across cilia microtubule inner proteins with implications for human cilia function," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    12. Cheoljun Choi & Yujin L. Jeong & Koung-Min Park & Minji Kim & Sangseob Kim & Honghyun Jo & Sumin Lee & Heeseong Kim & Garam Choi & Yoon Ha Choi & Je Kyung Seong & Sik Namgoong & Yeonseok Chung & Young, 2024. "TM4SF19-mediated control of lysosomal activity in macrophages contributes to obesity-induced inflammation and metabolic dysfunction," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    13. Yesheng Fu & Lei Li & Xin Zhang & Zhikang Deng & Ying Wu & Wenzhe Chen & Yuchen Liu & Shan He & Jian Wang & Yuping Xie & Zhiwei Tu & Yadi Lyu & Yange Wei & Shujie Wang & Chun-Ping Cui & Cui Hua Liu & , 2024. "Systematic HOIP interactome profiling reveals critical roles of linear ubiquitination in tissue homeostasis," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    14. 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.
    15. Hong-Wen Tang & Kerstin Spirohn & Yanhui Hu & Tong Hao & István A. Kovács & Yue Gao & Richard Binari & Donghui Yang-Zhou & Kenneth H. Wan & Joel S. Bader & Dawit Balcha & Wenting Bian & Benjamin W. Bo, 2023. "Next-generation large-scale binary protein interaction network for Drosophila melanogaster," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    16. Gergo Gogl & Boglarka Zambo & Camille Kostmann & Alexandra Cousido-Siah & Bastien Morlet & Fabien Durbesson & Luc Negroni & Pascal Eberling & Pau Jané & Yves Nominé & Andras Zeke & Søren Østergaard & , 2022. "Quantitative fragmentomics allow affinity mapping of interactomes," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    17. Hyoungjun Ham & Jacob B. Hirdler & Daniel T. Bihnam & Zhiming Mao & Joanina K. Gicobi & Bruna Gois Macedo & Maria F. Rodriguez-Quevedo & Destiny F. Schultz & Cristina Correia & Jun Zhong & Kodi E. Mar, 2025. "Lysosomal NKG7 restrains mTORC1 activity to promote CD8+ T cell durability and tumor control," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    18. Raamesh Deshpande & Benjamin VanderSluis & Chad L Myers, 2013. "Comparison of Profile Similarity Measures for Genetic Interaction Networks," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
    19. Shilin Sun & Hua Tian & Runze Wang & Zehua Zhang, 2023. "Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning," Mathematics, MDPI, vol. 11(3), pages 1-14, February.
    20. Adrià Fernández-Torras & Miquel Duran-Frigola & Martino Bertoni & Martina Locatelli & Patrick Aloy, 2022. "Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque," Nature Communications, Nature, vol. 13(1), pages 1-18, 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:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59175-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.