Recent works

Learning protein constitutive motifs from sequence data

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 (alpha-helixes and beta-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.

Application of Restricted Boltzmann Machines to the modeling of chaperon protein HSP70. The insets show two features inferred by the RBM from sequence data. On each site (x-axis in the inset, positions shown by balls on the protein 3D structure) the letter height indicate the relevance (positive or negative) of the corresponding amino acid to the feature; gaps are shown with squares. The top feature codes for a beta-sheet structural motif, while the bottom feature codes for the presence/absence of a loop crucial for function. Courtesy of J. Tubiana.

see J. Tubiana, S. Cocco, R. Monasson, eLife 2019;8:e39397 (2019).