Quantile distributions of amino acid usage in protein classes.

A comparative study of the compositional properties of various protein sets from both cellular and viral organisms is presented. Invariants and contrasts of amino acid usages have been discerned for different protein function classes and for different species using robust statistical methods based on quantile distributions and stochastic ordering relationships. In addition, a quantitative criterion to assess amino acid compositional extremes relative to a reference protein set is proposed and applied. Invariants of amino acid usage relate mainly to the central range of quantile distributions, whereas contrasts occur mainly in the tails of the distributions, especially contrasts between eukaryote and prokaryote species. Influences from genomic constraint are evident, for example, in the arginine:lysine ratios and the usage frequencies of residues encoded by G + C-rich versus A + T-rich codon types. The structurally similar amino acids, glutamate versus aspartate and phenylalanine versus tyrosine, show stochastic dominance relationships for most species protein sets favoring glutamate and phenylalanine respectively. The quantile distribution of hydrophobic amino acid usages in prokaryote data dominates the corresponding quantile distribution in human data. In contrast, glutamate, cysteine, proline and serine usages in human proteins dominate the corresponding quantile distributions in Escherichia coli. E. coli dominates human in the use of basic residues, but no dominance ordering applies to acidic residues. The discussion centers on commonalities and anomalies of the amino acid compositional spectrum in relation to species, function, cellular localization, biochemical and steric attributes, complexity of the amino acid biosynthetic pathway, amino acid relative abundances and founder effects

Estimating evolutionary distances from spaced-word matches

International audienceAlignment-free methods are increasingly used to estimate distances between DNA and protein sequences and to reconstruct phylogenetic trees. Most distance functions used by these methods, however, are heuristic measures of dissimilarity, not based on any explicit model of evolution. Herein, we propose a simple estimator of the evolutionary distance between two DNA sequences calculated from the number of (spaced) word matches between them. We show that this distance function estimates the evolutionary distance between DNA sequences more accurately than other distance measures used by alignment-free methods. In addition, we calculate the variance of the number of (spaced) word matches depending on sequence length and mismatch probability