Naslov
Clustering of protein domains for functional and evolutionary studies

Autori
Goldstein, Pavle ; Žučko, Jurica ; Vujaklija, Dušica ; Kriško, Anita ; Hranueli, Daslav ; Long, Paul F. ; Etchebest, Catherine ; Basrak, Bojan ; Cullum, John

Izvornik
BMC bioinformatics (1471-2105) 10 (2009); 335, 11

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
protein families ; DNA sequences ; sequence criteria ; evolutionary split statistic ; clustering algorithm

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Biologija, Biotehnologija

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