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Open Access Methodology article

Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets

Aydin Albayrak1, Hasan H Otu23 and Ugur O Sezerman1*

Author Affiliations

1 Biological Sciences and Bioengineering, Sabanci University, Orhanli, Tuzla, Istanbul, Turkey

2 Department of Medicine, BIDMC Genomics Center, Harvard Medical School, Boston, MA 02115, USA

3 Department of Bioengineering, Istanbul Bilgi University, 34060, Istanbul, Turkey

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BMC Bioinformatics 2010, 11:428  doi:10.1186/1471-2105-11-428

Published: 18 August 2010

Abstract

Background

Phylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering.

Results

We demonstrate the robustness of RCM with reduced alphabets in clustering of protein sequences into families in a simulated dataset and seven well-characterized protein datasets. On protein datasets, crotonases, mandelate racemases, nucleotidyl cyclases and glycoside hydrolase family 2 were clustered into subfamilies with 100% accuracy whereas acyl transferase domains, haloacid dehalogenases, and vicinal oxygen chelates could be assigned to subfamilies with 97.2%, 96.9% and 92.2% accuracies, respectively.

Conclusions

The overall combination of methods in this paper is useful for clustering protein families into subtypes based on solely protein sequence information. The method is also flexible and computationally fast because it does not require multiple alignment of sequences.