Protein Sectors: Statistical Coupling Analysis versus Conservation
By Tiberiu Teşileanu, Lucy J. Colwell, Stanislas Leibler. 2014
75 citations recorded by the source corpus.
Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.
Subfields: Molecular Biology
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In Molecular Biology
- Anne-Florence Bitbol
- Vincenzo Carnevale
- Cristian Micheletti
- Ned S. Wingreen
- Jean-Pierre Eckmann
- Tsvi Tlusty
- Shou-Wen Wang
- Albert Libchaber
Citations in the corpus
- Revealing evolutionary constraints on proteins through sequence analysis (2018, background): “Selection on multiple traits An important challenge in sector analysis is distinguishing multiple, independently evolving sectors [4,7, 52].”
- Evolution-Based Functional Decomposition of Proteins (2015, unknown): “Indeed, it is difficult to experimentally test the unique value of statistical coevolution by conventional single mutation experiments, even when conducted on a massive scale [21, 45, 46].”
- Patterns of coevolving amino acids unveil structural and dynamical domains (2017, methodology): “By construction, the method prioritizes the most conserved residues (88).”
- Physical model of the genotype-to-phenotype map of proteins (2016, background): “It is instructive to note similarities and differences between the spectra.”
- Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “It should be noted that while SCA reveals important residue networks within proteins, sequence conservation, rather than covariation, dominates sector determination in proteins with single sectors, meaning that the use of SCA to define sectors is only warranted in systems that have more than one sec”