Evolution-Based Functional Decomposition of Proteins

By Olivier Rivoire, Kimberly A. Reynolds, Rama Ranganathan. 2015

135 citations recorded by the source corpus.

The essential biological properties of proteins - folding, biochemical activities, and the capacity to adapt - arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function, but requires broad further testing by the scientific community. To facilitate this, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package. We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment - a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for understanding the structural basis for protein function and for generally testing the concept of sectors as the principal functional units of proteins.

Subfields: Molecular Biology

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “ted by ICOD and those measured experimentally at each site (ICOD-Exp; data from Fig. 5(c) for = 0 :02); between mutational effects predicted by ICOD and conservation scores ˚ l(ICOD-Conserv; see Ref. [7] and Eq. S62 in the binary case); and between experimentally measured mutational effects and cons”
Coevolution-based inference of amino acid interactions underlying protein function (2018, background): “0 [Rivoire et al., 2016]), SCA reveals a sparse internal organization in which most positions evolve in a nearly independent manner and a few (~20%) are engaged in a pattern of mutual coevolution (Halabi et al.”
Inferring interaction partners from protein sequences using mutual information (2018, background): “However, correlations in protein sequences also have 23 important collective modes [18, 19], which can arise from functional selection [20, 21], and addi- 24 tional correlations are due to phylogeny [18, 22, 23].”
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”