Evolution-Based Functional Decomposition of Proteins

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

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. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). 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 studying proteins and for generally testing the concept of sectors as the principal units of function and adaptive variation.

Original languageEnglish (US)
Article numbere1004817
JournalPLoS Computational Biology
Volume12
Issue number6
DOIs
StatePublished - Jun 1 2016

Fingerprint

protein degradation
Sector
decomposition
Amino Acids
Amino acids
Decomposition
Proteins
Protein
Decompose
amino acid
amino acids
protein
proteins
Python
Boidae
coevolution
Unit
Coevolution
Interaction
amino acid sequences

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience

Cite this

Evolution-Based Functional Decomposition of Proteins. / Rivoire, Olivier; Reynolds, Kimberly A.; Ranganathan, Rama.

In: PLoS Computational Biology, Vol. 12, No. 6, e1004817, 01.06.2016.

Research output: Contribution to journalArticle

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