Co-inertia analysis of amino-acid physico-chemical properties and protein composition with the ADE package
A multivariate analysis method called co-inertia analysis was used to determine the main relationships between two data tables having identical rows. This method is available in the ADE multivariate analysis package for Macintosh micro-computers. It was applied to two data sets, one containing the a...
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Published in | Bioinformatics Vol. 11; no. 3; pp. 321 - 329 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Washington, DC
Oxford University Press
01.06.1995
Oxford Oxford University Press (OUP) |
Subjects | |
Online Access | Get full text |
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Summary: | A multivariate analysis method called co-inertia analysis was used to determine the main relationships between two data tables having identical rows. This method is available in the ADE multivariate analysis package for Macintosh micro-computers. It was applied to two data sets, one containing the amino-acid composition of 999 E. coli proteins, and the other the values of 402 physico-chemical properties for the 20 natural amino-acids. There were strong relationships between amino-acid physico-chemical properties and the composition of proteins. The first common factor was hydrophobicity; it is linked to the biological environment of proteins, either in the cytoplasm (or outside the cell), or in the nonpolar environment of the phospholipid bilayer of biological membranes. The second factor linked the expressivity of protein genes and the propensity of amino-acids to form alpha helix/beta sheets. The third factor showed that heavy, aromatic amino-acids tend to be avoided, except when they are needed for structural or functional reasons. These results are discussed in terms of selective pressure acting on amino-acid composition of proteins. |
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Bibliography: | ArticleID:11.3.321 1To whom reprint requests should be addressed istex:F3770EBB82A3FA045DCAE1253D5F52062AA20611 ark:/67375/HXZ-M6GHJQPL-4 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1367-4803 0266-7061 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/11.3.321 |