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Learning protein constitutive motifs from sequence data.

Jérôme Tubiana1, Simona Cocco1, Rémi Monasson1

  • 1Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR 8023 & PSL Research, Paris, France.

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|March 13, 2019
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Summary
This summary is machine-generated.

Restricted Boltzmann Machines (RBM) effectively model protein families from sequence data. These machine learning models reveal interpretable biological features and enable the design of novel protein sequences with desired properties.

Keywords:
coevolutioncomputational biologymachine learningnonephysics of living systemssequence analysissystems biology

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Area of Science:

  • Computational biology
  • Machine learning in bioinformatics
  • Protein sequence analysis

Background:

  • Statistical analysis of protein sequences aids understanding of structure, function, and evolution.
  • Machine learning models, particularly Restricted Boltzmann Machines (RBM), excel at learning complex, high-dimensional data and statistical patterns.

Purpose of the Study:

  • To investigate the efficacy of Restricted Boltzmann Machines (RBM) in modeling protein families using sequence information.
  • To explore the biological interpretability of features learned by RBM.
  • To assess the potential of RBM for designing novel protein sequences.

Main Methods:

  • Application of Restricted Boltzmann Machines (RBM) to 20 diverse protein families, including Kunitz, WW, and Hsp70 domains, as well as synthetic proteins.
  • Analysis of RBM-inferred features for biological relevance to protein structure, function, and phylogeny.
  • Utilizing RBM's learned modes to design new protein sequences with specific properties.

Main Results:

  • RBM successfully modeled protein families, demonstrating efficiency in capturing statistical features from sequence data.
  • Inferred features were biologically interpretable, correlating with tertiary contacts, secondary motifs (alpha-helices, beta-sheets), disordered regions, activity, ligand specificity, and phylogenetic identity.
  • RBM facilitated the design of new protein sequences by manipulating learned modes, suggesting potential for property-specific design.

Conclusions:

  • Restricted Boltzmann Machines (RBM) are versatile and practical tools for analyzing protein sequence data.
  • RBM can effectively unveil the genotype-phenotype relationship within protein families.
  • The study highlights RBM's utility in both understanding existing protein diversity and designing novel proteins.