Research article

Automated Alphabet Reduction for Protein Datasets

Jaume Bacardit^1,2 , Michael Stout^1,2 , Jonathan D Hirst³ , Alfonso Valencia⁴ , Robert E Smith⁵ and Natalio Krasnogor¹

¹  ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

²  MYCIB, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK

³  School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK

⁴  Spanish National Cancer Research Centre, Melchor Fdez Almagro, 3. 28029 Madrid, Spain

⁵  Dept. of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

author email corresponding author email

BMC Bioinformatics 2009, 10:6doi:10.1186/1471-2105-10-6

Published:	6 January 2009

Abstract

Background

We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques.

Results

We applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations.

Conclusion

Our automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.