IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0062732.html
   My bibliography  Save this article

Semi-Supervised Prediction of SH2-Peptide Interactions from Imbalanced High-Throughput Data

Author

Listed:
  • Kousik Kundu
  • Fabrizio Costa
  • Michael Huber
  • Michael Reth
  • Rolf Backofen

Abstract

Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.

Suggested Citation

  • Kousik Kundu & Fabrizio Costa & Michael Huber & Michael Reth & Rolf Backofen, 2013. "Semi-Supervised Prediction of SH2-Peptide Interactions from Imbalanced High-Throughput Data," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-15, May.
  • Handle: RePEc:plo:pone00:0062732
    DOI: 10.1371/journal.pone.0062732
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0062732
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0062732&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0062732?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. Peter Blume-Jensen & Tony Hunter, 2001. "Oncogenic kinase signalling," Nature, Nature, vol. 411(6835), pages 355-365, May.
    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. Qiwei Jiang & Xiaomei Zhang & Xiaoming Dai & Shiyao Han & Xueji Wu & Lei Wang & Wenyi Wei & Ning Zhang & Wei Xie & Jianping Guo, 2022. "S6K1-mediated phosphorylation of PDK1 impairs AKT kinase activity and oncogenic functions," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Sichun Yang & Benoît Roux, 2008. "Src Kinase Conformational Activation: Thermodynamics, Pathways, and Mechanisms," PLOS Computational Biology, Public Library of Science, vol. 4(3), pages 1-14, March.
    3. Zhongtao Zhao & Qiaojun Jin & Jin-Rong Xu & Huiquan Liu, 2014. "Identification of a Fungi-Specific Lineage of Protein Kinases Closely Related to Tyrosine Kinases," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.
    4. Gianna Maria Nardi & Elisabetta Ferrara & Ilaria Converti & Francesca Cesarano & Salvatore Scacco & Roberta Grassi & Antonio Gnoni & Felice Roberto Grassi & Biagio Rapone, 2020. "Does Diabetes Induce the Vascular Endothelial Growth Factor (VEGF) Expression in Periodontal Tissues? A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-16, April.
    5. Hipólito Nicolás Cuesta-Hernández & Julia Contreras & Pablo Soriano-Maldonado & Jana Sánchez-Wandelmer & Wayland Yeung & Ana Martín-Hurtado & Inés G. Muñoz & Natarajan Kannan & Marta Llimargas & Javie, 2023. "An allosteric switch between the activation loop and a c-terminal palindromic phospho-motif controls c-Src function," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    6. Hui-Rong Xu & Zhong-Fa Xu & Yan-Lai Sun & Jian-Jun Han & Zeng-Jun Li, 2013. "The −842G/C Polymorphisms of PIN1 Contributes to Cancer Risk: A Meta-Analysis of 10 Case-Control Studies," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-7, August.
    7. Martin Klammer & J Nikolaj Dybowski & Daniel Hoffmann & Christoph Schaab, 2015. "Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    8. Jeongah Yoon & Thomas S Deisboeck, 2009. "Investigating Differential Dynamics of the MAPK Signaling Cascade Using a Multi-Parametric Global Sensitivity Analysis," PLOS ONE, Public Library of Science, vol. 4(2), pages 1-14, February.

    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:pone00:0062732. 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.