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The use of Self-Organizing Maps and genetic algorithms to investigate a proteomes

Amino acid composition of proteins can be modelled by both selectional and mutational pressures. The influence of selection is related with functional and structural requirements on proteins, whereas mutations modify directly nucleotide composition of protein coding sequences. To study the influence of these pressures on amino acid composition and its dependence from evolutionary relationships and ecological properties of almost 200 prokaryotic microorganisms, Kohonen neural networks (Self-Organizing Maps, SOM) were applied. This approach was chosen in order to compare amino acid composition of proteins contextually and find compositional patterns/prototypes of particular proteomes, which were encoded in centroids of learned neurons.