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 single-site statistics . It has been reported in a number of studies that the protein sectors found by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. We analyze the available experimental data and show that for proteins where a single SCA sectoris identified, the functionally-significant residues can also be found using single-site statistics such as conservation. We thus point to the need for more data for the cases in which several sectors are predicted by SCA .
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 .
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single-site statistics
sequence conservation
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reported in a number of studies
asserted
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found
identified
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We analyze
Here we reconsider
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show that for proteins where a single SCA sectoris identified, the functionally-significant residues can also be found using single-site statistics such as conservation. We thus point to
note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is
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need for more data for the cases in which several sectors are predicted by SCA
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