Goldstein, P;
Zucko, J;
Vujaklija, D;
Krisko, A;
Hranueli, D;
Long, PF;
Etchebest, C;
... Cullum, J; + view all
(2009)
Clustering of protein domains for functional and evolutionary studies.
BMC Bioinformatics
, 10
(Februa)
, Article 335. 10.1186/1471-2105-10-335.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Clustering of protein domains for functional and evolutionary studies. |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/1471-2105-10-335 |
| Publisher version: | http://dx.doi.org./10.1186/1471-2105-10-335 |
| Language: | English |
| Additional information: | © 2009 Goldstein et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1363854 |
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