CLASSIFICATIONS OF AMINO ACIDS IN PROTEINS BY THE SELF-ORGANIZING MAP

Mookyung Cheon, Muyoung Heo, Iksoo Chang () and Choongrak Kim
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Mookyung Cheon: National Research Laboratory for Computational Proteomics and Biophysics, and Department of Physics, Pusan National University, Busan 609-735, Korea
Muyoung Heo: National Research Laboratory for Computational Proteomics and Biophysics, and Department of Physics, Pusan National University, Busan 609-735, Korea
Iksoo Chang: National Research Laboratory for Computational Proteomics and Biophysics, and Department of Physics, Pusan National University, Busan 609-735, Korea
Choongrak Kim: Department of Statistics, Pusan National University, Busan 609-735, Korea

International Journal of Modern Physics C (IJMPC), 2005, vol. 16, issue 10, 1609-1616

Abstract: We present the clustering properties of amino acids, which are building blocks of proteins, according to their physico-chemical characters. To classify the 20 kinds of amino acids, we employ a Self-Organizing Map (SOM) analysis for the Miyazawa-Jernigan (MJ) pairwise-contact matrix, the Environment-dependent One-body energy Parameters (EOP) and the one-body energy parameters incorporating the Ramachandran angle information (EOPR) over the EOP in proteins. We provide the new result of the SOM clustering for amino acids based on the EOPR and compare that with those from the MJ and the EOP matrix. All three kinds of energy parameters capture the leading role played by the hydrophobicity and the hydrophilicity of amino acids in protein folding. Our SOM analysis generally illustrates that both the EOP and the EOPR can provide the collective clustering of amino acids by the side chain characteristics and the secondary structure information. However, EOP is better at classifying amino acids according to their side chain characteristics whereas EOPR is better with secondary structure. We show that the EOP and the EOPR matrix manifests more detailed physico-chemical classification of amino acids than those from the MJ matrix, which does not contain a local environmental information of amino acids in the protein structures.

Keywords: Self-Organizing Map; protein energy parameters; classification of amino acids; hydrophobicity scales (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S0129183105008175

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