IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36736-1.html
   My bibliography  Save this article

Hierarchical graph learning for protein–protein interaction

Author

Listed:
  • Ziqi Gao

    (The Hong Kong University of Science and Technology
    The Hong Kong University of Science and Technology)

  • Chenran Jiang

    (Shenzhen Bay Laboratory)

  • Jiawen Zhang

    (The Hong Kong University of Science and Technology)

  • Xiaosen Jiang

    (The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences)

  • Lanqing Li

    (AI Lab, Tencent)

  • Peilin Zhao

    (AI Lab, Tencent)

  • Huanming Yang

    (The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences)

  • Yong Huang

    (The Hong Kong University of Science and Technology)

  • Jia Li

    (The Hong Kong University of Science and Technology
    The Hong Kong University of Science and Technology)

Abstract

Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and understanding of PPIs. Despite recent progress, in silico methods remain inadequate in modeling the natural PPI hierarchy. Here we present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better capture the structure-function relationship of the protein. HIGH-PPI examines both outside-of-protein and inside-of-protein of the human interactome to establish a robust machine understanding of PPIs. This model demonstrates high accuracy and robustness in predicting PPIs. Moreover, HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely. Overall, “HIGH-PPI [ https://github.com/zqgao22/HIGH-PPI ]” is a domain-knowledge-driven and interpretable framework for PPI prediction studies.

Suggested Citation

  • Ziqi Gao & Chenran Jiang & Jiawen Zhang & Xiaosen Jiang & Lanqing Li & Peilin Zhao & Huanming Yang & Yong Huang & Jia Li, 2023. "Hierarchical graph learning for protein–protein interaction," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36736-1
    DOI: 10.1038/s41467-023-36736-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36736-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-36736-1?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. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. Charles P. Couturier & Shamini Ayyadhury & Phuong U. Le & Javad Nadaf & Jean Monlong & Gabriele Riva & Redouane Allache & Salma Baig & Xiaohua Yan & Mathieu Bourgey & Changseok Lee & Yu Chang David Wa, 2020. "Author Correction: Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    3. Joshua H. Siegle & Xiaoxuan Jia & Séverine Durand & Sam Gale & Corbett Bennett & Nile Graddis & Greggory Heller & Tamina K. Ramirez & Hannah Choi & Jennifer A. Luviano & Peter A. Groblewski & Ruweida , 2021. "Survey of spiking in the mouse visual system reveals functional hierarchy," Nature, Nature, vol. 592(7852), pages 86-92, April.
    4. István A. Kovács & Katja Luck & Kerstin Spirohn & Yang Wang & Carl Pollis & Sadie Schlabach & Wenting Bian & Dae-Kyum Kim & Nishka Kishore & Tong Hao & Michael A. Calderwood & Marc Vidal & Albert-Lász, 2019. "Network-based prediction of protein interactions," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    5. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    6. Evi Hendrikx & Jacob M. Paul & Martijn Ackooij & Nathan Stoep & Ben M. Harvey, 2022. "Visual timing-tuned responses in human association cortices and response dynamics in early visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    7. Klemens Engelberg & Tyler Bechtel & Cynthia Michaud & Eranthie Weerapana & Marc-Jan Gubbels, 2022. "Proteomic characterization of the Toxoplasma gondii cytokinesis machinery portrays an expanded hierarchy of its assembly and function," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Charles P. Couturier & Shamini Ayyadhury & Phuong U. Le & Javad Nadaf & Jean Monlong & Gabriele Riva & Redouane Allache & Salma Baig & Xiaohua Yan & Mathieu Bourgey & Changseok Lee & Yu Chang David Wa, 2020. "Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy," Nature Communications, Nature, vol. 11(1), pages 1-19, December.
    9. Nicolas Renaud & Cunliang Geng & Sonja Georgievska & Francesco Ambrosetti & Lars Ridder & Dario F. Marzella & Manon F. Réau & Alexandre M. J. J. Bonvin & Li C. Xue, 2021. "DeepRank: a deep learning framework for data mining 3D protein-protein interfaces," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    10. Youhui Zhang & Peng Qu & Yu Ji & Weihao Zhang & Guangrong Gao & Guanrui Wang & Sen Song & Guoqi Li & Wenguang Chen & Weimin Zheng & Feng Chen & Jing Pei & Rong Zhao & Mingguo Zhao & Luping Shi, 2020. "A system hierarchy for brain-inspired computing," Nature, Nature, vol. 586(7829), pages 378-384, October.
    11. Vladimir Gligorijević & P. Douglas Renfrew & Tomasz Kosciolek & Julia Koehler Leman & Daniel Berenberg & Tommi Vatanen & Chris Chandler & Bryn C. Taylor & Ian M. Fisk & Hera Vlamakis & Ramnik J. Xavie, 2021. "Structure-based protein function prediction using graph convolutional networks," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    12. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    13. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    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. Jiahua Rao & Jiancong Xie & Qianmu Yuan & Deqin Liu & Zhen Wang & Yutong Lu & Shuangjia Zheng & Yuedong Yang, 2024. "A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions," Nature Communications, Nature, vol. 15(1), pages 1-12, 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. 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.
    2. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    3. Yu, Jiating & Wu, Ling-Yun, 2022. "Multiple Order Local Information model for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Aziz, Furqan & Gul, Haji & Muhammad, Ishtiaq & Uddin, Irfan, 2020. "Link prediction using node information on local paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    5. Simon L. Dürr & Andrea Levy & Ursula Rothlisberger, 2023. "Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. Zhou, Tao & Lee, Yan-Li & Wang, Guannan, 2021. "Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    7. Stefanie Duller & Simone Vrbancic & Łukasz Szydłowski & Alexander Mahnert & Marcus Blohs & Michael Predl & Christina Kumpitsch & Verena Zrim & Christoph Högenauer & Tomasz Kosciolek & Ruth A. Schmitz , 2024. "Targeted isolation of Methanobrevibacter strains from fecal samples expands the cultivated human archaeome," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    8. Lee, Yan-Li & Dong, Qiang & Zhou, Tao, 2021. "Link prediction via controlling the leading eigenvector," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    9. Yu, Jiating & Leng, Jiacheng & Sun, Duanchen & Wu, Ling-Yun, 2023. "Network Refinement: Denoising complex networks for better community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    10. Julia Koehler Leman & Pawel Szczerbiak & P. Douglas Renfrew & Vladimir Gligorijevic & Daniel Berenberg & Tommi Vatanen & Bryn C. Taylor & Chris Chandler & Stefan Janssen & Andras Pataki & Nick Carrier, 2023. "Sequence-structure-function relationships in the microbial protein universe," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Marco Malatesta & Emanuele Fornasier & Martino Luigi Salvo & Angela Tramonti & Erika Zangelmi & Alessio Peracchi & Andrea Secchi & Eugenia Polverini & Gabriele Giachin & Roberto Battistutta & Roberto , 2024. "One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative AI," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, National Bureau of Economic Research, Inc.
    13. Deyun Qiu & Jinxin V. Pei & James E. O. Rosling & Vandana Thathy & Dongdi Li & Yi Xue & John D. Tanner & Jocelyn Sietsma Penington & Yi Tong Vincent Aw & Jessica Yi Han Aw & Guoyue Xu & Abhai K. Tripa, 2022. "A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    14. Shuo-Shuo Liu & Tian-Xia Jiang & Fan Bu & Ji-Lan Zhao & Guang-Fei Wang & Guo-Heng Yang & Jie-Yan Kong & Yun-Fan Qie & Pei Wen & Li-Bin Fan & Ning-Ning Li & Ning Gao & Xiao-Bo Qiu, 2024. "Molecular mechanisms underlying the BIRC6-mediated regulation of apoptosis and autophagy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. Xiaoke Yang & Mingqi Zhu & Xue Lu & Yuxin Wang & Junyu Xiao, 2024. "Architecture and activation of human muscle phosphorylase kinase," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    16. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    17. Efren Garcia-Maldonado & Andrew D. Huber & Sergio C. Chai & Stanley Nithianantham & Yongtao Li & Jing Wu & Shyaron Poudel & Darcie J. Miller & Jayaraman Seetharaman & Taosheng Chen, 2024. "Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    18. Kristy Rochon & Brianna L. Bauer & Nathaniel A. Roethler & Yuli Buckley & Chih-Chia Su & Wei Huang & Rajesh Ramachandran & Maria S. K. Stoll & Edward W. Yu & Derek J. Taylor & Jason A. Mears, 2024. "Structural basis for regulated assembly of the mitochondrial fission GTPase Drp1," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    19. Katherine A. Ray & Joshua D. Lutgens & Ramesh Bista & Jie Zhang & Ronak R. Desai & Melissa Hirsch & Takeshi Miyazawa & Antonio Cordova & Adrian T. Keatinge-Clay, 2024. "Assessing and harnessing updated polyketide synthase modules through combinatorial engineering," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    20. Fan Lu & Liang Zhu & Thomas Bromberger & Jun Yang & Qiannan Yang & Jianmin Liu & Edward F. Plow & Markus Moser & Jun Qin, 2022. "Mechanism of integrin activation by talin and its cooperation with kindlin," Nature Communications, Nature, vol. 13(1), pages 1-19, 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:14:y:2023:i:1:d:10.1038_s41467-023-36736-1. 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.