IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0065587.html
   My bibliography  Save this article

The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Author

Listed:
  • Guilherme T Valente
  • Marcio L Acencio
  • Cesar Martins
  • Ney Lemke

Abstract

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.

Suggested Citation

  • Guilherme T Valente & Marcio L Acencio & Cesar Martins & Ney Lemke, 2013. "The Development of a Universal In Silico Predictor of Protein-Protein Interactions," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0065587
    DOI: 10.1371/journal.pone.0065587
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065587
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0065587&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0065587?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. Benjamin A Shoemaker & Anna R Panchenko, 2007. "Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners," PLOS Computational Biology, Public Library of Science, vol. 3(4), pages 1-7, April.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    4. Xinyi Liu & Bin Liu & Zhimin Huang & Ting Shi & Yingyi Chen & Jian Zhang, 2012. "SPPS: A Sequence-Based Method for Predicting Probability of Protein-Protein Interaction Partners," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-6, January.
    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. Saeid Rasti & Chrysafis Vogiatzis, 2019. "A survey of computational methods in protein–protein interaction networks," Annals of Operations Research, Springer, vol. 276(1), pages 35-87, May.

    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. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Rafiee, Samira & Salavati, Chiman & Abdollahpouri, Alireza, 2020. "CNDP: Link prediction based on common neighbors degree penalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    4. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    5. Shugang Li & Ziming Wang & Beiyan Zhang & Boyi Zhu & Zhifang Wen & Zhaoxu Yu, 2022. "The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    6. Kai Yang & Yuan Liu & Zijuan Zhao & Xingxing Zhou & Peijin Ding, 2023. "Graph attention network via node similarity for link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-10, March.
    7. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    8. 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).
    9. Wang, Feifei & Dong, Jiaxin & Lu, Wanzhao & Xu, Shuo, 2023. "Collaboration prediction based on multilayer all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 17(1).
    10. Guan-Nan Wang & Hui Gao & Lian Chen & Dennis N A Mensah & Yan Fu, 2015. "Predicting Positive and Negative Relationships in Large Social Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    11. Byungun Yoon & Songhee Kim & Sunhye Kim & Hyeonju Seol, 2022. "Doc2vec-based link prediction approach using SAO structures: application to patent network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5385-5414, September.
    12. Cai, Wenbo & Chang, Xingzhi & Yang, Ping, 2024. "Link prediction in multilayer social networks using reliable local random walk and boosting ensemble classifier," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
    13. Najari, Shaghayegh & Salehi, Mostafa & Ranjbar, Vahid & Jalili, Mahdi, 2019. "Link prediction in multiplex networks based on interlayer similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    14. Zhang, Ting & Zhang, Kun & Li, Xun & Lv, Laishui & Sun, Qi, 2021. "Semi-supervised link prediction based on non-negative matrix factorization for temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    15. Zhou, Tao, 2023. "Discriminating abilities of threshold-free evaluation metrics in link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    16. Tad Dallas & Andrew W Park & John M Drake, 2017. "Predicting cryptic links in host-parasite networks," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-15, May.
    17. Ding, Rui & Ujang, Norsidah & Hamid, Hussain bin & Manan, Mohd Shahrudin Abd & He, Yuou & Li, Rong & Wu, Jianjun, 2018. "Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 800-817.
    18. Chunning Wang & Fengqin Tang & Xuejing Zhao, 2023. "LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    19. Wahid-Ul-Ashraf, Akanda & Budka, Marcin & Musial, Katarzyna, 2019. "How to predict social relationships — Physics-inspired approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1110-1129.
    20. Chen, Guangfu & Xu, Chen & Wang, Jingyi & Feng, Jianwen & Feng, Jiqiang, 2020. "Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

    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:pone00:0065587. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.