PHOSIDA 2011: the posttranslational modification database - PubMed
. 2011 Jan;39(Database issue):D253-60.
doi: 10.1093/nar/gkq1159. Epub 2010 Nov 16.
Affiliations
- PMID: 21081558
- PMCID: PMC3013726
- DOI: 10.1093/nar/gkq1159
PHOSIDA 2011: the posttranslational modification database
Florian Gnad et al. Nucleic Acids Res. 2011 Jan.
Abstract
The primary purpose of PHOSIDA (http://www.phosida.com) is to manage posttranslational modification sites of various species ranging from bacteria to human. Since its last report, PHOSIDA has grown significantly in size and evolved in scope. It comprises more than 80,000 phosphorylated, N-glycosylated or acetylated sites from nine different species. All sites are obtained from high-resolution mass spectrometric data using the same stringent quality criteria. One of the main distinguishing features of PHOSIDA is the provision of a wide range of analysis tools. PHOSIDA is comprised of three main components: the database environment, the prediction platform and the toolkit section. The database environment integrates and combines high-resolution proteomic data with multiple annotations. High-accuracy species-specific phosphorylation and acetylation site predictors, trained on the modification sites contained in PHOSIDA, allow the in silico determination of modified sites on any protein on the basis of the primary sequence. The toolkit section contains methods that search for sequence motif matches or identify de novo consensus, sequences from large scale data sets.
Figures

The new browsing function allows the searching for posttranslationally modified proteins that were identified in a particular experiment or cell type. Furthermore, the gene ontology filter enables users to search for modified proteins with specific cellular localization and molecular function (left panel). Selecting one of the resulting protein entries (middle panel) yields the protein annotation web page (right panel). In addition to general protein information, identified posttranslational modification sites are listed. Clicking on one of the site buttons results in the provision of site-specific information. In the illustrated example, searching for protein kinases that are both phosphorylated and N-glycosylated in the mouse brain and localized in the plasma membrane yields a list of proteins that match the specified search criteria. One of these proteins is the insulin receptor.

On the site level PHOSIDA provides the surrounding sequence, matching motifs, the predicted secondary structure, the predicted accessibility, the corresponding identified peptides and the identification state in certain cell types (left panel: N-glycosylated asparagine on position 426 of the mouse glutamate receptor 2 subunit) or experiments (right panel: serine on position 1039 of the human EGF receptor).

Species-specific phosphorylation or acetylation site predictors allow the in silico identification of proteins based on the primary sequence. Users can insert the sequence of a single protein or the sequences of multiple proteins in FASTA format (left panel). Using specified precision recall values, the predicted posttranslational modification sites are listed (right panel).

The Motif Matcher searches for sequence matches with annotated motifs including kinase recognition patterns. Users can insert a single sequence or multiple sequences (left panel) to find motif matches (right panel).

The Motif Finder identifies significantly overrepresented consensus sequences in given large-scale phospho data sets. It compares the position specific amino acid frequencies in the input set (left panel) with the ones of the background set. Based on bootstrap statistics the motif finder extracts de novo sequence motifs and matches them with annotated kinase motifs (right panel).
Similar articles
-
Gnad F, Ren S, Cox J, Olsen JV, Macek B, Oroshi M, Mann M. Gnad F, et al. Genome Biol. 2007;8(11):R250. doi: 10.1186/gb-2007-8-11-r250. Genome Biol. 2007. PMID: 18039369 Free PMC article.
-
Predicting post-translational lysine acetylation using support vector machines.
Gnad F, Ren S, Choudhary C, Cox J, Mann M. Gnad F, et al. Bioinformatics. 2010 Jul 1;26(13):1666-8. doi: 10.1093/bioinformatics/btq260. Epub 2010 May 26. Bioinformatics. 2010. PMID: 20505001 Free PMC article.
-
Quantitative phosphoproteomic analysis of the PI3K-regulated signaling network.
Gnad F, Wallin J, Edgar K, Doll S, Arnott D, Robillard L, Kirkpatrick DS, Stokes MP, Vijapurkar U, Hatzivassiliou G, Friedman LS, Belvin M. Gnad F, et al. Proteomics. 2016 Jul;16(14):1992-7. doi: 10.1002/pmic.201600118. Epub 2016 Jul 8. Proteomics. 2016. PMID: 27282143
-
Munk S, Refsgaard JC, Olsen JV, Jensen LJ. Munk S, et al. Methods Mol Biol. 2016;1355:307-21. doi: 10.1007/978-1-4939-3049-4_21. Methods Mol Biol. 2016. PMID: 26584935 Review.
-
Prediction of posttranslational modification of proteins from their amino acid sequence.
Eisenhaber B, Eisenhaber F. Eisenhaber B, et al. Methods Mol Biol. 2010;609:365-84. doi: 10.1007/978-1-60327-241-4_21. Methods Mol Biol. 2010. PMID: 20221930 Review.
Cited by
-
Bansal N, Zhang M, Bhaskar A, Itotia P, Lee E, Shlyakhtenko LS, Lam TT, Fritz A, Berezney R, Lyubchenko YL, Stafford WF, Thapar R. Bansal N, et al. Biochemistry. 2013 Jan 22;52(3):520-36. doi: 10.1021/bi301074r. Epub 2013 Jan 11. Biochemistry. 2013. PMID: 23286197 Free PMC article.
-
Activity regulation of adenosine deaminases acting on RNA (ADARs).
Orlandi C, Barbon A, Barlati S. Orlandi C, et al. Mol Neurobiol. 2012 Feb;45(1):61-75. doi: 10.1007/s12035-011-8220-2. Epub 2011 Nov 20. Mol Neurobiol. 2012. PMID: 22113393 Review.
-
Fonslow BR, Niessen SM, Singh M, Wong CC, Xu T, Carvalho PC, Choi J, Park SK, Yates JR 3rd. Fonslow BR, et al. J Proteome Res. 2012 May 4;11(5):2697-709. doi: 10.1021/pr300200x. Epub 2012 Apr 17. J Proteome Res. 2012. PMID: 22509746 Free PMC article.
-
Nadarajan S, Altendorfer E, Saito TT, Martinez-Garcia M, Colaiácovo MP. Nadarajan S, et al. Proc Natl Acad Sci U S A. 2021 Apr 27;118(17):e2016363118. doi: 10.1073/pnas.2016363118. Proc Natl Acad Sci U S A. 2021. PMID: 33883277 Free PMC article.
-
Martins-de-Souza D, Alsaif M, Ernst A, Harris LW, Aerts N, Lenaerts I, Peeters PJ, Amess B, Rahmoune H, Bahn S, Guest PC. Martins-de-Souza D, et al. BMC Res Notes. 2012 Mar 15;5:146. doi: 10.1186/1756-0500-5-146. BMC Res Notes. 2012. PMID: 22420779 Free PMC article.
References
-
- Hunter T. Signaling–2000 and beyond. Cell. 2000;100:113–127. - PubMed
-
- Pawson T, Nash P. Assembly of cell regulatory systems through protein interaction domains. Science. 2003;300:445–452. - PubMed
-
- Choudhary C, Mann M. Decoding signalling networks by mass spectrometry-based proteomics. Nature Reviews. 2010;11:427–439. - PubMed
-
- Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell. 2006;127:635–648. - PubMed
-
- Daub H, Olsen JV, Bairlein M, Gnad F, Oppermann FS, Korner R, Greff Z, Keri G, Stemmann O, Mann M. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell. 2008;31:438–448. - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources