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Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

Abstract

Proteins carry out a broad range of functions in living organisms usually by interacting with other molecules. Protein–protein interaction (PPI) is an important base for understanding disease mechanisms and for deciphering rational drug design. The identification of protein interactions using experimental methods is expensive and time-consuming. Therefore, efficient computational methods to predict PPIs are of great value to biologists.

This work focuses on predicting protein interfaces and investigates the effect of different molecular representations in the prediction of such sites. We introduce a molecular representation according to its hierarchical structure. Therefore, proteins are abstracted in terms of spatial and sequential neighboring among amino acid pairs, while we use a deep learning framework, Graph Convolutional Networks, for data training. We tested the framework on two classes of proteins, Antibody–Antigen and Antigen–Bound Antibody, extracted from the Protein–Protein Docking Benchmark 5.0. The obtained results in terms of the area under the ROC curve (AU-ROC) on these classes are remarkable.

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Funding

This research has been partially supported by the University of Padua project BIRD189710/18 “Reliable identification of the PPI interface in multiunit protein complexes”.

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Via Gradenigo 6/A, 35131, Padova (PD), Italy

    Michela Quadrini, Sebastian Daberdaku & Carlo Ferrari

  2. Sorint.Tek, Sorint.LAB Group, Via Giovanni Savelli 102, 35129, Padova (PD), Italy

    Sebastian Daberdaku

Authors
  1. Michela Quadrini
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  2. Sebastian Daberdaku
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  3. Carlo Ferrari
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Corresponding author

Correspondence to Michela Quadrini .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Quadrini, M., Daberdaku, S., Ferrari, C. (2020). Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_34

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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