Journal
BMC BIOINFORMATICS
Volume 10, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1471-2105-10-335
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Funding
- iProject [8045 M047]
- Ministry of Science, Education and Sports, Republic of Croatia [037-0982913-2762, 098-0982913-2877, 058-0000000-3475]
- German Academic Exchange Service (DAAD)
- Leverhulme Trust,
- Japanese Bio-Industry Association
- The School of Pharmacy, University of London
- UNESCO and L'Oreal
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Background: The number of protein family members defined by DNA sequencing is usually much larger than those characterised experimentally. This paper describes a method to divide protein families into subtypes purely on sequence criteria. Comparison with experimental data allows an independent test of the quality of the clustering. Results: An evolutionary split statistic is calculated for each column in a protein multiple sequence alignment; the statistic has a larger value when a column is better described by an evolutionary model that assumes clustering around two or more amino acids rather than a single amino acid. The user selects columns (typically the top ranked columns) to construct a motif. The motif is used to divide the family into subtypes using a stochastic optimization procedure related to the deterministic annealing EM algorithm (DAEM), which yields a specificity score showing how well each family member is assigned to a subtype. The clustering obtained is not strongly dependent on the number of amino acids chosen for the motif. The robustness of this method was demonstrated using six well characterized protein families: nucleotidyl cyclase, protein kinase, dehydrogenase, two polyketide synthase domains and small heat shock proteins. Phylogenetic trees did not allow accurate clustering for three of the six families. Conclusion: The method clustered the families into functional subtypes with an accuracy of 90 to 100%. False assignments usually had a low specificity score.
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