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A Deep Learning-Based Method for Uncovering GPCR Ligand-Induced Conformational States Using Interpretability Techniques

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

There is increasing interest in the development of tools for investigating the protein ligand space. Understanding the underlying mechanisms of G protein-coupled receptors (GPCR) in the ligand-binding process is of particular interest due to their role in pharmacoproteomics. In this work, we propose the study of GPCR ligand-induced conformational variations from Molecular Dynamics (MD) simulations using Deep Learning (DL)-based methods. We devise and train a Convolutional Neural Network (CNN) for classifying the states for both ligand-free structure and the bound of agonists in the \(\beta 2\)-adrenergic receptor. We also study the transformation of MD data into an interaction network matrix to further improve and facilitate the analyses without significant loss of information. Our method introduces a framework for the study of the effect of ligand-receptor binding activity that includes a novel analysis based on interpretability algorithms, allowing the quantification of the contribution of individual residues to structural re-arrangements.

This research is partially funded by research grant PID2019-104551RB-I00.

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Authors and Affiliations

  1. Computer Science Department, and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain

    Mario A. Gutiérrez-Mondragón, Caroline König & Alfredo Vellido

Authors
  1. Mario A. Gutiérrez-Mondragón
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  2. Caroline König
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  3. Alfredo Vellido
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Corresponding author

Correspondence to Mario A. Gutiérrez-Mondragón .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Gutiérrez-Mondragón, M.A., König, C., Vellido, A. (2022). A Deep Learning-Based Method for Uncovering GPCR Ligand-Induced Conformational States Using Interpretability Techniques. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_23

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  • Online ISBN: 978-3-031-07802-6

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