Alignment-Independent Techniques for Protein Classification

Authors: Davies, Matthew N.; Secker, Andrew; Freitas, Alex A.; Timmis, Jon; Clark, Edward; Flower, Darren R.

Source: Current Proteomics, Volume 5, Number 4, December 2008 , pp. 217-223(7)

Publisher: Bentham Science Publishers

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Abstract:

Predicting protein structure and function from amino acid sequences is a central aim of bioinformatics. Most bioinformatics analyses use sequence alignment as the basis by which to measure similarity. However, there is increasing evidence that many protein families are resistant to this straightforward method of comparison. Increasingly, a combination of machine-learning techniques and abstract representations of protein sequences is being used to classify proteins based upon the similarity of their physico-chemical properties rather than scoring sequence alignments. This is particularly effective in protein families that show greater structural conservation but appear to lack conserved sequences. Here we describe the inherent limitations of the alignment-dependent approaches to protein classification and present `alignment- free' representations as a viable and realistic alternative to solve complex problems within bioinformatics.

Keywords: Multiple alignment; protein motifs; alignment independent; protein classification; discrete form; sequential form; local descriptors; proteochemometrics

Document Type: Research article

DOI: http://dx.doi.org/10.2174/157016408786733770

Publication date: 2008-12-01

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Current Proteomics research in the emerging field of proteomics is growing at an extremely rapid rate. The principal aim of Current Proteomics is to publish well-timed review articles in this fast-expanding area on topics relevant and significant to the development of proteomics. Current Proteomics is an essential journal for everyone involved in proteomics and related fields in both academia and industry.