DIPLOMARBEIT
UNIVERSITÄTSZENTRUM INFORMATIK
Martin-Luther-Universität Halle-Wittenberg
Klassifizierung von Aminosäuren auf der Basis von
Strukturdaten
Alexander Entzian
(2008)
 Universitätszentrum Informatik • Universität Halle
von-Seckendorff-Platz 1 • 06120 Halle (Saale)
Allgemeine Angaben
Die Diplomarbeit wurde am Lehrstuhl für Theoretische Chemie von Prof. Dr. Horst
Bögel, Institut für Organische Chemie der Martin-Luther-Universität angefertigt.
Zur Kontaktaufnahme benutzen Sie bitte die Email-Adresse
direktor@uzi.uni-halle.de.
Zusammenfassung
The considered 20 canonical amino acids show a complicated spectrum of their
properties; they are traditionally divided according to their acid and bases strength,
the hydrophobicity, and qualities of the substituent of the side chain, as well as other
qualities. These divisions are based on the chemical experience which leads to numerous overlapping of possible separations into classes.
In this work additional characteristic features obtained by theoretical calculations are
considered, such as stabilization energy, electron densities (or atomic charges), partial charges areas on the molecule surface and front orbital energies. The most stable
conformation of amino acids were calculated by structural optimization and conformation analysis, assuming the molecular mechanics on the basis of proven force field
parameter (MMFF94x). These structures were re-optimized with semi-empirical
methods (MOPAC) by MOE [1]. From these calculation we have analysed the electronic structure and the sequence of the orbital energies. In case of a chiral centre
only the S-configurations were considered owing to their biological meaning.
A suitable set of features were selected from the calculated qualities by means of
Principal Component Analyse (PCA). We used an agglomerative hierarchical clustering using the software program XLSTAT [2]. This is an unbiased classification
achieved with mathematical-statistical methods. In contrast to traditional divisions the
degree of the resemblance of the amino acids gets more apparent.
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The best classification could be reached by using 7 features in ordered to divides to
20 amino acids into 4 classes which could be compared to the Venn diagram drawn
by K. Giles [3]. We hope to use this classification for comparing with the BLOSUM62
matrix [4].
[1] Chemical Computing Group Inc., Molecular-Operating-Environment (MOE),
Version 2005.06 und 2007.09. – URL: http://www.chemcomp.com
[2] Addinsoft, XLSTAT 2007. – URL: http://www.xlstat.com
[3] K. Giles, Voreingestellten Klassifizierungen von Rasmol im Venn-Diagramm. –
URL: http://www.bohne-lang.de/spec/linux/rasmol/venn.gif
[4] S. Henikoff, J. G. Henikoff, Prac. Natl. Acad. Sci., 1992, 89, 10915-10919
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