Learning protein constitutive motifs from sequence data

  1. Jérôme Tubiana
  2. Simona Cocco
  3. Rémi Monasson  Is a corresponding author
  1. École Normale Supérieure, France

Abstract

Statistical analysis of evolutionary-related protein sequences provides insights about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to twenty protein families, and present detailed results for two short protein domains, Kunitz and WW, one long chaperone protein, Hsp70, and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (such as residue-residue tertiary contacts, extended secondary motifs (α-helix and β-sheet) and intrinsically disordered regions), to function (such as activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and turning up or down the different modes at will. Our work therefore shows that RBM are a versatile and practical tool to unveil and exploit the genotype-phenotype relationship for protein families.

Data availability

The Python 2.7 package for training and visualizing RBMs, used to obtained the results reported in this work, is available at https://github.com/jertubiana/ProteinMotifRBM. It can be readily used for any protein family. Moreover, all four multiple sequence alignments presented in the text, as well as the code for reproducing each panel are also included. Jupyter notebooks are provided for reproducing most figures of the article.

The following previously published data sets were used

Article and author information

Author details

  1. Jérôme Tubiana

    Laboratoire de Physique Statistique, École Normale Supérieure, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8878-5620
  2. Simona Cocco

    Laboratoire de Physique Statistique, École Normale Supérieure, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Rémi Monasson

    Laboratoire de Physique Théorique, École Normale Supérieure, Paris, France
    For correspondence
    monasson@lpt.ens.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4459-0204

Funding

Centre National de la Recherche Scientifique

  • Jérôme Tubiana
  • Simona Cocco
  • Rémi Monasson

Ecole Normale Supérieure (Allocation Specifique)

  • Jérôme Tubiana

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Lucy J Colwell, Cambridge University, United Kingdom

Publication history

  1. Received: October 3, 2018
  2. Accepted: February 24, 2019
  3. Accepted Manuscript published: March 12, 2019 (version 1)
  4. Version of Record published: March 27, 2019 (version 2)

Copyright

© 2019, Tubiana et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Jérôme Tubiana
  2. Simona Cocco
  3. Rémi Monasson
(2019)
Learning protein constitutive motifs from sequence data
eLife 8:e39397.
https://doi.org/10.7554/eLife.39397

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