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T4SS Effector Protein Prediction with Deep Learning

Author

Listed:
  • Koray Açıcı

    (Department of Computer Engineering, Baskent University, Bağlıca Kampüsü Fatih Sultan Mahallesi Eskişehir Yolu 18.km, Ankara 06709, Turkey)

  • Tunç Aşuroğlu

    (Department of Computer Engineering, Baskent University, Bağlıca Kampüsü Fatih Sultan Mahallesi Eskişehir Yolu 18.km, Ankara 06709, Turkey)

  • Çağatay Berke Erdaş

    (Department of Computer Engineering, Baskent University, Bağlıca Kampüsü Fatih Sultan Mahallesi Eskişehir Yolu 18.km, Ankara 06709, Turkey)

  • Hasan Oğul

    (Department of Computer Engineering, Baskent University, Bağlıca Kampüsü Fatih Sultan Mahallesi Eskişehir Yolu 18.km, Ankara 06709, Turkey
    Faculty of Computer Science, Østfold University College, P.O. Box 700, 1757 Halden, Norway)

Abstract

Extensive research has been carried out on bacterial secretion systems, as they can pass effector proteins directly into the cytoplasm of host cells. The correct prediction of type IV protein effectors secreted by T4SS is important, since they are known to play a noteworthy role in various human pathogens. Studies on predicting T4SS effectors involve traditional machine learning algorithms. In this work we included a deep learning architecture, i.e., a Convolutional Neural Network (CNN), to predict IVA and IVB effectors. Three feature extraction methods were utilized to represent each protein as an image and these images fed the CNN as inputs in our proposed framework. Pseudo proteins were generated using ADASYN algorithm to overcome the imbalanced dataset problem. We demonstrated that our framework predicted all IVA effectors correctly. In addition, the sensitivity performance of 94.2% for IVB effector prediction exhibited our framework’s ability to discern the effectors in unidentified proteins.

Suggested Citation

  • Koray Açıcı & Tunç Aşuroğlu & Çağatay Berke Erdaş & Hasan Oğul, 2019. "T4SS Effector Protein Prediction with Deep Learning," Data, MDPI, vol. 4(1), pages 1-13, March.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:1:p:45-:d:216917
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    References listed on IDEAS

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    1. David Burstein & Tal Zusman & Elena Degtyar & Ram Viner & Gil Segal & Tal Pupko, 2009. "Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach," PLOS Pathogens, Public Library of Science, vol. 5(7), pages 1-12, July.
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    Cited by:

    1. Sergey Dudarov & Elena Guseva & Yury Lemetyuynen & Ilya Maklyaev & Boris Karetkin & Svetlana Evdokimova & Pavel Papaev & Natalia Menshutina & Victor Panfilov, 2022. "Fundamentals and Applications of Artificial Neural Network Modelling of Continuous Bifidobacteria Monoculture at a Low Flow Rate," Data, MDPI, vol. 7(5), pages 1-19, May.

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