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The majority of scientific resources are devoted to studying a relatively small number of model species, meaning that the ability to translate knowledge across species is of considerable importance. Obtaining species-specific knowledge enables targeted investigations of the biology and pathobiology of a particular species, and facilitates comparative analyses. Phosphorylation is the most widespread posttranslational modification in eukaryotes, and although many phosphorylation sites have been experimentally identified for some species, little or no data are available for others. Using the honeybee as a test organism, this case study illustrates the process of using protein sequence homology to identify putative phosphorylation sites in a species of interest using experimentally determined sites from other species. A number of issues associated with this process are examined and discussed. Several databases of experimentally determined phosphorylation sites exist; however, it can be difficult for the nonspecialist to ascertain how their contents compare. Thus, this case study assesses the content and comparability of several phosphorylation site databases. Additional issues examined include the efficacy of homology-based phosphorylation site prediction, the impact of the level of evolutionary relatedness between species in making these predictions, the ability to translate knowledge of phosphorylation sites across large evolutionary distances and the criteria that should be used in selecting probable phosphorylation sites in the species of interest. Although focusing on phosphorylation, the issues discussed here also apply to the homology-based cross-species prediction of other posttranslational modifications, as well as to sequence motifs in general. © The Author 2014. Published by Oxford University Press. For Permissions, please email:


Brett Trost, Scott Napper, Anthony Kusalik. Case study: using sequence homology to identify putative phosphorylation sites in an evolutionarily distant species (honeybee). Briefings in bioinformatics. 2015 Sep;16(5):820-9

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PMID: 25380664

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