Learning protein constitutive motifs from sequence data
By Jérôme Tubiana, Simona Cocco, Rémi Monasson. 2018
115 citations recorded by the source corpus.
Statistical analysis of evolutionary-related protein sequences provides information 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 20 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 (residue-residue tertiary contacts, extended secondary motifs (α-helixes and β-sheets) and intrinsically disordered regions), to function (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 'turning down' the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype–phenotype relationship for protein families.
Subfields: Molecular Biology
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In Molecular Biology
- Martin Weigt
- Andrea Pagnani
- Guido Uguzzoni
- Alessandro Barducci
- Duccio Malinverni
- Francesco Zamponi
- J. Trinquier
- Kyle Cranmer
Citations in the corpus
- Efficient generative modeling of protein sequences using simple autoregressive models (2021, methodology): “Latent-variable models like Restricted Boltzmann machines [55] or Hopfield-Potts models [48] learn dimensionally reduced representations of proteins; using sequence motifs, they are able to capture groups of collectively evolving residues [44] better than DCA models, but are less accurate in extract”
- Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting (2019, background): “In a more recent development, authors of [25] introduced the use of restricted Boltzmann machines to simultaneously identify subfamilies and their characterizing motifs.”
- Machine learning and the physical sciences (2019, methodology): “Insights from this work were more recently used to model protein families from their sequence information (Tubiana et al., 2018).”