Clustering of protein domains for functional and evolutionary studies. Export

@article{citeulike:5948414, 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.}, author = {Goldstein, Pavle and Zucko, Jurica and Vujaklija, Dusica and Krisko, Anita and Hranueli, Daslav and Long, Paul F. and Etchebest, Catherine and Basrak, Bojan and Cullum, John}, citeulike-article-id = {5948414}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-335}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19832975}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19832975}, doi = {10.1186/1471-2105-10-335}, issn = {1471-2105}, journal = {BMC bioinformatics}, keywords = {clustering, domain, evolution, protein}, number = {1}, pages = {335+}, posted-at = {2009-10-19 09:07:40}, priority = {2}, title = {Clustering of protein domains for functional and evolutionary studies.}, url = {http://dx.doi.org/10.1186/1471-2105-10-335}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:5948414 L3 - citeulike-article-id:5948414 N2 - 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. IS - 1 JF - BMC bioinformatics SN - 1471-2105 TI - Clustering of protein domains for functional and evolutionary studies. VL - 10 SP - 335 KW - clustering KW - domain KW - evolution KW - protein AU - Goldstein, Pavle AU - Zucko, Jurica AU - Vujaklija, Dusica AU - Krisko, Anita AU - Hranueli, Daslav AU - Long, Paul AU - Etchebest, Catherine AU - Basrak, Bojan AU - Cullum, John PY - 2009/// UR - http://dx.doi.org/10.1186/1471-2105-10-335 ER -