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Bioinformatics Advance Access published online on November 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti806
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 30, 2005
Revised November 27, 2005
Accepted November 27, 2005

Article

Application of compression-based distance measures to protein sequence classification: a methodological study

András Kocsor 1 *, Attila Kertész-Farkas 1, László Kaján 2, and Sándor Pongor 3

1 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1., H-6720 Szeged, Hungary
2 Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, I-34012 Trieste, Italy
3 Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, I-34012 Trieste, Italy; Bioinformatics Group, Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62, H-6701 Szeged, Hungary

* To whom correspondence should be addressed.
András Kocsor, E-mail: kocsor{at}inf.u-szeged.hu


  Abstract

Motivation: Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences.

Results: We constructed Compression-based Distance Measures (CBMs) using the Lempel-Ziv and the PPMZ compression algorithms and compared their performance with that of the Smith-Waterman algorithm and BLAST, using nearest neighbour (1NN) or support vector machine (SVM) classification schemes. The datasets included a subset of the SCOP protein structure database to test distant protein similarities, a 3-phosphoglycerate-kinase sequences selected from archaean, bacterial and eukaryotic species as well as low and high-complexity sequence segments of the human proteome. CBMs values show a dependence on the length and the complexity of the sequences compared. In classification tasks CBMs performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith-Waterman algorithm and two Hidden Markov Model-based algorithms.

Availability: www.inf.u-szeged.hu/~kocsor/CBM05 (supplementary data)


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