Trost Brett, Maleki Farhad, Kusalik Anthony, Napper Scott
Vaccine and Infectious Disease Organization, ‡Department of Computer Science, and §Department of Biochemistry, University of Saskatchewan , Saskatoon, SK S7N 5A2, Canada.
J Proteome Res. 2016 Aug 5;15(8):2760-7. doi: 10.1021/acs.jproteome.6b00304. Epub 2016 Jul 13.
The post-translational modification of proteins is critical for regulating their function. Although many post-translational modification sites have been experimentally determined, particularly in certain model organisms, experimental knowledge of these sites is severely lacking for many species. Thus, it is important to be able to predict sites of post-translational modification in such species. Previously, we described DAPPLE, a tool that facilitates the homology-based prediction of one particular post-translational modification, phosphorylation, in an organism of interest using known phosphorylation sites from other organisms. Here, we describe DAPPLE 2, which expands and improves upon DAPPLE in three major ways. First, it predicts sites for many post-translational modifications (20 different types) using data from several sources (15 online databases). Second, it has the ability to make predictions approximately 2-7 times faster than DAPPLE depending on the database size and the organism of interest. Third, it simplifies and accelerates the process of selecting predicted sites of interest by categorizing them based on gene ontology terms, keywords, and signaling pathways. We show that DAPPLE 2 can successfully predict known human post-translational modification sites using, as input, known sites from species that are either closely (e.g., mouse) or distantly (e.g., yeast) related to humans. DAPPLE 2 can be accessed at http://saphire.usask.ca/saphire/dapple2 .
蛋白质的翻译后修饰对于调节其功能至关重要。尽管许多翻译后修饰位点已通过实验确定,特别是在某些模式生物中,但对于许多物种而言,关于这些位点的实验知识严重匮乏。因此,能够预测此类物种中的翻译后修饰位点非常重要。此前,我们描述了DAPPLE,这是一种工具,可利用来自其他生物的已知磷酸化位点,促进基于同源性预测感兴趣生物中一种特定的翻译后修饰——磷酸化。在此,我们描述了DAPPLE 2,它在三个主要方面对DAPPLE进行了扩展和改进。首先,它使用来自多个来源(15个在线数据库)的数据预测多种翻译后修饰(20种不同类型)的位点。其次,根据数据库大小和感兴趣的生物,它进行预测的速度比DAPPLE快约2至7倍。第三,它通过基于基因本体术语、关键词和信号通路对预测的感兴趣位点进行分类,简化并加速了选择过程。我们表明,DAPPLE 2可以使用与人类密切相关(如小鼠)或远距离相关(如酵母)物种的已知位点作为输入,成功预测已知的人类翻译后修饰位点。可通过http://saphire.usask.ca/saphire/dapple2访问DAPPLE 2。