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 language | English (US) |
|---|---|
| Article number | e1004817 |
| Journal | PLoS Computational Biology |
| Volume | 12 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1 2016 |
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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 journal › Article
}
TY - JOUR
T1 - Evolution-Based Functional Decomposition of Proteins
AU - Rivoire, Olivier
AU - Reynolds, Kimberly A.
AU - Ranganathan, Rama
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1004817
DO - 10.1371/journal.pcbi.1004817
M3 - Article
VL - 12
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 6
M1 - e1004817
ER -