PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites

By Florian Gnad, Shubin Ren, Jüergen Cox, Jesper V. Olsen, Boris Macek, Mario Oroshi, Matthias Mann. Genome Biology, 2007

479 citations recorded by the source corpus.

PHOSIDA http://www.phosida.com, a phosphorylation site database, integrates thousands of high-confidence in vivo phosphosites identified by mass spectrometry-based proteomics in various species. For each phosphosite, PHOSIDA lists matching kinase motifs, predicted secondary structures, conservation patterns, and its dynamic regulation upon stimulus. Using support vector machines, PHOSIDA also predicts phosphosites.

Subfields: Genetics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/db6d93a34981212757c8cf2859bfc6a0fee80c08

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. A. Baucom
2. Gerard Manning
3. Zemin Zhang
4. Kiran Mukhyala
5. Lars Juhl Jensen
6. Florian Gnad

In Molecular Biology

1. Matthias Mann
2. Chunaram Choudhary
3. Zsuzsanna Dosztányi
4. John R. Yates III
5. C. Ruse
6. A. Nakorchevsky
7. Florian Gnad
8. Toby J. Gibson

In Spectroscopy

1. Chunaram Choudhary
2. Matthias Mann
3. John R. Yates III
4. C. Ruse
5. A. Nakorchevsky
6. Florian Gnad
7. Lars Juhl Jensen
8. Allegra Via

Citations in the corpus

• Decoding signalling networks by mass spectrometry-based proteomics (2010, methodology): “Therefore, proteomic PTM data sets should contain both a peptide identification score and a PTM localization scor”
• Using graph theory to analyze biological networks (2011, unknown): “ELM [31], NetPhorest [32] or PHOSIDA [33].”
• Phospho.ELM: a database of phosphorylation sites—update 2011 (2010, background): “Both PHOSIDA (47) and phosphoPEP (48) are specialized in annotation of large-scale experiments; phosphoGRID for Saccharomyces cerevisiae (49), virPTM for viruses (50) and P 3 DB for various plants (51) are devoted to specific species.”
• Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, background): “For example, a multitude of solvent accessible residues such as posttranslational modification sites are fundamental for protein function, which is reflected in their conservation [23,24], but not in their structural context.”
• PHOSIDA 2011: the posttranslational modification database (2010, unknown): “The evolutionary section provides information about the phylogenetic relationships between modified proteins and homologs in other species as described (8).”
• Proteomics by mass spectrometry: approaches, advances, and applications. (2009, methodology): “Mapped phosphorylation sites and their temporal profiles were deposited in a phosphorylation-site resource database (250).”
• Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database (2011, background): “Step(3)generatestworawstatisticsfilesthatarepost-processedinStep(4)byrank ordering and categorizing the results, as well as preparing them to be sent to the webpage.”
• 15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms (2018, background): “Multiple types of evidence in PSP can help identify functionally important sites including: the evolutionary conservation of a modification site (10,11); the frequency with which a PTM has been observed in unbiased tandem mass spectrometry (MS2) experiments, which correlates with the conservation of”