Beyond mutations: accounting for selection and self-organization in the analysis of protein evolution

Molecular phylogenetic research has relied on the analysis of the coding
sequences by genes or of the amino acid sequences by the encoded proteins.
Enumerating the numbers of mismatches, being indicators of mutation, has
been central to pertinent algorithms. However, the constraining forces of
selection and self-organization have been unaccounted for in conventional
approaches, possibly causing available models to fall short of
representing the actual evolutionary history. Specific amino acids possess
quantifiable characteristics that enable the conversion from “words”
(strings of letters denoting amino acids or bases) to “waves” (strings of
quantitative values representing the physico-chemical properties) or to
matrices (coordinates representing the positions in a comprehensive
property space). The application of such numerical representations to
evolutionary analysis takes into account not only mutation but also
selection/self-organization as influences that drive speciation, because
selective pressures favor certain mutations over others, and this
predilection is represented in the characteristics of the incorporated
amino acids (it is not born out solely by the mismatches). Besides being
more discriminating sources for treegenerating algorithms than
match/mismatch, the number strings can be examined for overall similarity
with average mutual information, autocorrelation, and fractal dimension.
Bivariate wavelet analysis aids in distinguishing hypermutable versus
conserved domains of the protein. Further, the matrix depiction is readily
subjected to comparisons of distances (Euclidean distance, Frobenius
distance), and it allows the generation of heat maps or graphs. These
analytical algorithms have been automated in R and are applicable to
various processes that are describable in matrix format.