Beyond mutations: Accounting for quantitative changes in the analysis of protein evolution

Area of Science:

• Molecular Biology
• Evolutionary Biology
• Bioinformatics

Background:

• Phylogenetic research traditionally analyzes gene or protein sequences by counting mismatches.
• This method overlooks the impact of mutation properties on evolutionary trajectories.

Purpose of the Study:

• To develop and present a new analytical framework for molecular phylogenetics.
• To incorporate physico-chemical properties of amino acids into evolutionary analyses.

Main Methods:

• Converting amino acid sequences into numerical representations (waves and matrices) based on quantifiable properties.
• Utilizing methods like average mutual information, autocorrelation, fractal dimension, and bivariate wavelet analysis.
• Applying matrix-based comparisons for distance analysis, heat maps, and graph generation.

Main Results:

• The new approach accounts for mutation properties, offering a more nuanced view of evolutionary drivers.
• Numerical representations provide more discriminating data for phylogenetic algorithms compared to simple mismatch counts.
• This method successfully resolved conflicting phylogenetic results for the S100A6 protein and aids in studying mitochondrial protein origins.

Conclusions:

• Numerical representation of amino acid properties offers a powerful alternative for molecular phylogenetic analysis.
• This method enhances the understanding of evolutionary processes by integrating mutation characteristics.
• The automated algorithms in R are versatile and applicable to various matrix-based biological processes.