Clustering of the protein design alphabets by using hierarchical self-organizing map

Twenty kinds of amino acids are building blocks for proteins. The classification of characters of amino acids helps to reduce the complexity of protein properties. Here, we present the classification of 20 amino acids by using hierarchical self-organizing map clustering with the Miyazawa-Jernigan pairwise-contact energy parameters. The classification not only gives the biological interpretations for each group, whose clusterings are in agreement with those of the previous works, but also provides more detailed and characteristic features of amino acid clustering. Hydrophobic, medium, loop-favoring, and polar amino acids are grouped, and each representative amino acid is identified. Hierarchical self-organizing map clustering is proven to be a good clustering tool for classifying the 20 amino acids.