Taxonomy of nuclear receptors and SERPINS by multivariate analysis of amino-acid composition

Abstract

The global amino-acid composition of a protein, although a cruder variable than sequence, is nevertheless informative and has been correlated with protein structural class. In the present study, we have applied complementary multivariate methods based on chi 2-metrics (correspondence factor analysis (CFA), minimum spanning tree (MST), ascending hierarchical classification (AHC)) to the analysis of the amino-acid frequency patterns of the C-terminal domain of 39 members of the nuclear receptor superfamily. The correlations we observed among receptors by this simple approach were, with few exceptions, in line with published phylogenetic dendrograms derived by sequence alignment. Further multivariate analyses were performed on the receptor population combined with 26 serine protease inhibitors (SERPINS) in view of the analogies detected between these superfamilies by hydrophobic cluster analysis (HCA), which were at the origin of the choice of alpha 1-antitrypsin as a 3-dimensional (3D) model for the receptor hormone-binding domain. Both the MST and AHC identified two distinct protein populations which in the principal phi 1 phi 2 CFA plot showed virtually no overlap, thus suggesting that receptors and SERPINS have different overall folding patterns, although the lower-order phi 3 phi 4 plot did reveal some similarities, essentially in the use of hydrophobic amino acids, that might account for analogies in HCA patterns. Receptors had a preference for those amino acids that are more frequent in alpha-helices and SERPINS for those in beta-strands and also tended to use different amino acids in turns. We therefore propose that multivariate analysis of amino-acid composition may prove helpful in identifying proteins for subsequent HCA.