Protein phosphorylation is one of the most important post-translational modifications (PTMs) and regulates a broad spectrum of biological processes. Recent progresses in phosphoproteomic identifications have generated a flood of phosphorylation sites, while the integration of these sites is an urgent need. In this work, we developed a curated database of dbPAF, containing known phosphorylation sites in H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae. From the scientific literature and public databases, we totally collected and integrated 54,148 phosphoproteins with 483,001 phosphorylation sites. Multiple options were provided for accessing the data, while original references and other annotations were also present for each phosphoprotein. Based on the new data set, we computationally detected significantly over-represented sequence motifs around phosphorylation sites, predicted potential kinases that are responsible for the modification of collected phospho-sites and evolutionarily analyzed phosphorylation conservation states across different species. Besides to be largely consistent with previous reports, our results also proposed new features of phospho-regulation. Taken together, our database can be useful for further analyses of protein phosphorylation in human and other model organisms. The dbPAF database was implemented in PHP + MySQL and freely available at http://dbpaf.biocuckoo.org.
As one of the most well-studied and ubiquitous post-translational modifications (PTMs) in proteins, phosphorylation is a conserved mechanism in both eukaryotes and prokaryotes, participates in almost all of biological processes and reversibly determines the cellular dynamics and plasticity1,2,3,4,5. In eukaryotes, phosphorylation mainly occurs on a subset of three types of amino acids, including serine, threonine and tyrosine residues. Phosphorylation events are dynamically but precisely regulated and the core machinery of a phosphorylation system contains numerous protein kinases (PKs) for modifying proteins as “writers”, phospho-binding proteins for recognizing and interacting with phosphorylation sites as “readers” and protein phosphatases (PPs) for demodifying substrates as “erasers”2,3,6,7. The aberrant phosphorylation has been extensively reported to be highly associated with human diseases, such as cancers8, neurodegenerative diseases9 and metabolic disorders10. In this regard, the identification and functional analysis of phosphosites are fundamental for understanding the molecular mechanisms and regulatory roles of protein phosphorylation.
Recently, rapid progresses in high-throughput liquid chromatography/mass spectrometry (LC-MS) techniques have greatly advanced the identification of phosphorylation sites11,12,13. For example, using a mouse model of multistage skin carcinogenesis, Zanivan et al. identified and quantified 3,457 proteins with 5,249 phosphorylation sites12. Also, using a label-free technology, Lundby et al. totally quantified 31,480 phosphorylation sites in 7,280 proteins across 14 rat organs and tissues13. Recently, Humphrey et al. developed a new platform of EasyPhos, which can rapidly quantify over 10,000 phosphorylation sites from cell or tissue samples in a single run11. Since a flood of sites have been generated, computational analysis of the phosphoproteomic data in a systematic level has also been boomed, such as the prediction of site-specific kinase-substrate relations (ssKSRs)14,15,16, the re-construction and modeling of phosphorylation networks1,17, the molecular evolutionary analysis of phosphorylation signalings6,18 and the identification of genetic variations that potentially change phosphorylation19,20. Thus, we can expect that more and more experimental and computational studies will be carried out in the near future, to accumulatively enhance our knowledge on the phosphorylation.
Due to the data accumulation, the collection and integration of numerous phosphorylated substrates with their sites from different studies have emerged to be a great challenge. The first phosphorylation database, PhosphoBase, was developed in 1998 and only contained 398 experimentally identified phosphorylation sites at that time21. In 2004, this database was merged into a new database of Phospho.ELM22,23 and its 9.0 version contained 41,070 phosphorylation sites in 10,601 substrates (Table 1). In 2006, two phosphorylation databases of dbPTM24 and PHOSIDA25 were released and both databases were continuously maintained nearly ten years until now26,27. To date, one of the most comprehensive and popular database is PhosphositePlus, which was initially established in 2004 and currently contained 310,779 phosphosites of 37,568 proteins28,29 (Table 1). In contrast to a general collection of phosphosites, several databases were mainly focused on specific species for a better annotation, because the number of known phosphorylation sites is still too limited in most of organisms. For example, PhosphoPep only maintained phosphorylation sites for 4 model organisms, including H. sapiens, D. melanogaster, C. elegans and S. cerevisiae30,31, while PhosphoGRID only collected known phosphosites in S. cerevisiae32,33. PHOSIDA was also organized in a species-specific manner, containing phosphorylation sites in nine prokaryotic or eukaryotic organisms25,26. In addition, although several databases, such as SysPTM34,35, HPRD36 and UniProt37 were constructed for a more general purpose, they also maintained a large number of phosphorylation sites.
During the past decade, we also collected and curated phosphorylation sites beyond directly using known data sets from public databases. Previously, we developed two phosphorylation databases of dbPPT5 and dbPSP4 for plants and prokaryotes, respectively. Here we reported a new database of dbPAF for the phospho-sites in human, animals and fungi. From the scientific literature and public databases, we totally collected and integrated 483,001 known phosphorylation sites in 54,148 proteins from H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae. The detailed annotations together with original references were provided for each protein entry. Using the new data set, we detected significantly over-represented sequence motifs around phospho-serine (pS), phospho-threonine (pT) and phospho-tyrosine (pY) sites for each species, separately. From the results, we observed that the most significant pT motifs are highly conserved across different organisms, while pS sites prefer to occur at intrinsically disordered regions with low-complexity. We also predicted potential PKs for phospho-sites and demonstrated that different PK groups or families play different roles in the regulation of phosphorylation. In addition, we systematically analyzed the conservation states of phosphorylation sites and observed that the phosphorylation is similarly conserved across different species. Taken together, the dbPAF database can serve as a useful resource for further studies of protein phosphorylation in human, animals and fungi.
Database construction and data statistics
In this work, we mainly focused on the collection and integration of known phosphosites identified from large-scale phosphoproteomic studies and the procedure for the implementation of dbPAF database was shown in Fig. 1. First, we searched the PubMed with multiple keywords, including “phosphoproteomics”, “phosphoproteomic” and “phosphoproteome”. All retrieved articles were carefully curated and we directly took the identified phosphorylated proteins, peptides and sites from the supplementary materials published together with these manuscripts if available. Because only a handful of eukaryotic species had the enough phosphorylation information, here we only reserved phosphorylation sites in H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae. Totally, we collected 294,370 non-redundant phosphorylation sites of 40,432 proteins from 115 published papers (Supplementary Table S1). For each species, we mapped corresponding phosphorylated proteins to its proteome sequences downloaded from UniProt database37 and the phosphorylation sites were exactly pinpointed (Fig. 1). The detailed annotations such as protein names, gene names, keywords, functional descriptions and sequence annotations of phosphoproteins were retrieved from UniProt and further integrated, while the original references of identified phosphorylation sites were also provided in dbPAF (Fig. 1).
Besides the manual curation of the literature, we also integrated known phosphorylation sites of the seven species from several public databases, including Phospho.ELM22,23, dbPTM24,27, PHOSIDA25,26 , PhosphositePlus28,29 , PhosphoPep30,31, PhosphoGRID32,33, SysPTM34,35, HPRD36 and UniProt37 (Table 1). For multiple entries with an identical phosphosite in the same protein, only one record was reserved. Finally, dbPAF contained 483,001 known phosphorylation sites of 54,148 proteins, as a comprehensive data resource for human, animals and fungi.
With the data set, we counted the distribution of phospho-sites for different amino acid types and observed that the phosphorylation predominantly modifies pS residues (65.84%), while only 22.49% and 11.67% of phosphorylation events occur in pT and pY sites, respectively (Fig. 2a). At the current stage, the phosphorylation events are not equally profiled in each species, whereas 50.52% (244,034 sites in 18,773 proteins) and 24.71% (119,328 sites in 14,044 proteins) of total phosphorylation sites were detected in H. sapiens and M. musculus, separately (Fig. 2b). In contrast, only 1,389 phosphorylated proteins with 3,957 sites were identified in S. pombe.
Usage of the dbPAF database
Our database was developed in an easy-to-use manner and multiple options were provided to access the phosphorylation information. First, phosphorylated substrates in dbPAF can be browsed in a species-specific mode (Fig. 3a). Here we chose human peroxisomal alkyldihydroxyacetonephosphate synthase (AGPS) as an example to demonstrate the usage of “Browse by species”. After clicking on the species diagram of H. sapiens (Fig. 3a), all human phosphorylated proteins would be listed in a tabular format with “dbPAF ID”, “UniProt Accession”, “Species” and “Protein Name” (Fig. 3b). A unique “dbPAF ID” was assigned to each protein for the convenient organization of dbPAF database. Then by clicking on the “dbPAF-0000003”, the detailed annotations including 18 known phospho-sites of human AGPS could be shown (Fig. 3c).
Besides the “Browse by species” option, our database provided up to four search options including “Substrate Search” (Fig. 4a), “Advanced search” (Fig. 4b), “Batch Search” (Fig. 4c) and “Blast Search” (Fig. 4d). For the “Substrate Search”, users can input one or multiple keywords, e.g, using “P53_HUMAN” and selecting “UniProt Accession”, to find the phosphorylation information of human p53 (Fig. 4a). Also, users can choose the ‘Advanced Search’ using three terms together with three operators of “and”, “or” and “exclude”, to obtain a more exact hit (Fig. 4b). Moreover, “Batch Search” was present for retrieving multiple phosphoproteins with a list of keywords (Fig. 4c). Finally, “Blast Search” was implemented to find identical or homologous proteins using a protein sequence in FASTA Format. The NCBI BLAST package38 was adopted for the sequence alignment (Fig. 4d).
The sequence preferences around phosphorylation sites
Previous studies demonstrated that short linear motifs around phorphorylation sites confer major specificity for the recognition, although a variety of cellular contextual factors, such as subcellular co-localization of PKs with their substrates, co-complex, or physical interaction, provide additional phosphorylation specificity in vivo1,7,15. Using the data set in dbPAF, here we performed a motif-based analysis of sequence preferences around phosphorylation sites in H. sapiens, M. musculus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae (Fig. 5). The motifs in R. norvegicus was not computationally detected, because human and mouse can be representative for mammalians. In the results, the most significant motifs of pY sites are quite different across different organisms, although the motif pYS in H. sapiens is considerably similar with pYSP in M. musculus (Fig. 5). A simple interpretation is that tyrosine phosphorylation was evolved in metazoans39 and there are no tyrosine kinases encoded in S. pombe and S. cerevisiae40. Thus, tyrosine phosphorylation in yeasts might be mediated by dual-specificity protein kinases which can phosphorylate both serine/threonine and tyrosine sites41. Also, during the evolution, the number of tyrosine kinases was significantly increased. For example, there are only 32 tyrosine kinases in D. melanogaster, but human has up to 90 tyrosine kinases40. Thus, different complexities of tyrosine phosphorylation in different species determine the distinct motif patterns. In contrast, the most significant motifs of pT sites in different species are quite similar (Fig. 5) and the results demonstrated a conserved mechanism for the threonine phosphorylation during evolution. Additionally, it was demonstrated that the phosphorylation sites preferentially occur in intrinsically disordered regions with low-complexity42. However, only most significant motifs of pS sites in H. sapiens, M. musculus and S. cerevisiae follow the intrinsically disordered sequences, whereas pS motifs in other species are still informative (Fig. 5).
To further dissect sequence preferences of mammalian phosphorylation, we systematically predicted potential PKs for phosphorylation sites in H. sapiens, M. musculus and R. norvegicus (Fig. 6). A previously developed tool of GPS 2.116 was used to predict ssKSRs in the family level (Fig. 6b) and then counted in the group level (Fig. 6a). In the kinase group level, the top four groups are AGC, CAMK, Other and CMGC kinase groups, which are responsible for the modification of about 70~75% of total phosphorylation events (Fig. 6a). In the family level, the top ten kinase families carry out about ~35% to 40% of total phosphorylation events (Fig. 6b). Thus, our results demonstrated that different kinase groups or families play distinct roles in mammalians and combinatorially determine the phosphorylation preferences.
The conservation distributions of phosphorylation sites
In a previous study, Minguez et al. developed the Residue Conservation Score (RCS) to determine the conservation status of PTM sites43. Because the overall conservation states of different proteins are not equal, they further used non-modified residues as a reference data set to normalize RCS and further calculated the relative RCS (rRCS) value for each PTM site. In their analysis, they totally collected ~93,000 phosphorylation sites, including 58,501, 20,880, 1,748, 1,951, 1,337 and 9,764 sites in H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans and S. cerevisiae, respectively. Based on the data set, the average rRCS values were calculated as about 55~85%, exhibiting a strong difference across eukaryotes43.
Here, with the newly integrated data set in dbPAF, we adopted the same procedure and re-performed the analysis of phosphorylation conservation in seven organisms (Fig. 7a). In contrast with the previously study43, our average rRCS values ranged from 73.10% (in S. pombe) to 84.34% (in R. norvegicus) and there was not a significant difference across different organisms (Fig. 7b, Table 2). For example, the average rRCS values are about 55~60% in D. melanogaster, C. elegans and S. cerevisiae, while our results are 76.65%, 75.50% and 75.72%, respectively (Fig. 7b). However, our average rRCS values on H. sapiens and M. musculus are similar with the previous study (Table 2). Thus, the previous results might be biased for lower species due to the data limitation43. When the data set was enlarged, each species shows a similar conservation status of phosphorylation. Also, for the pY conservation, we observed that the rRCS values in mammalians are much higher, while the ones in lower species are <80% (Fig. 7b). Again, this is because the complexity of tyrosine phosphorylation regulation is much higher in mammalians with more tyrosine kinases39,40 and thus mammalian tyrosine phospho-sites undergo a stronger functional constraint during the evolution.
Recent progresses in the development and improvement of high-throughput phosphoproteomic techniques have facilitated a rapid increase of the number of identified phosphorylated proteins and sites in animals, fungi, plants5 and prokaryotes4. Due to the data accumulation, computational analysis of the big data has also emerged to be an intriguing topic, in contrast with conventionally experimental assays1,6,14,15,16,17,18,19,20. All of these studies are heavily dependent on a high quality data resource of phospho-sites. Although a number of public databases were developed22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37, no one can collect and maintain all known phospho-sites. Also, several databases were developed for a more general purpose. For example, SysPTM 2.0 contained modification sites for 50 types of PTMs across 2031 species34,35, whereas HPRD is one of the most useful resource for human proteins but not limited to PTMs36. UniProt is a popular database for protein annotations and PTMs are just one part of features37. In addition, a considerable proportion of databases were developed for a general collection of phosphorylation sites, while only PhosphoPep30,31, PhosphoGRID32,33 and PHOSIDA25,26 were constructed for specific organisms with a rich data set, since few phospho-sites were reported in most of species. Thus, the collection, integration and annotation of phosphorylation sites in an organism-specific manner can be highly useful for further studies of phosphorylation in a specific species.
In this work, we reported a new database of dbPAF with 483,001 known phosphorylation sites in 54,148 proteins from human, animals and fungi. By comparison, our database contained more phospho-sites than other databases (Table 1). Because phosphorylated proteins, peptides and sites from different papers or databases might be differentially processed and annotated with distinct criteria, it’s a great challenge to ensure the standardization of data quality. Although several pilot studies have been performed for the quality control of phosphoproteomic data sets, the experimental evidences, e.g., the use of phospho-specific antibodies to verify the existence of normalized phosphosites44, yet remain to be provided. Because this study was mainly focused on the collection and integration of known phosphosites, any further normalization or standardization of the data set was not carried out. Based on the newly integrated data set, we carefully analyzed the sequence motifs around pS, pT and pY sites (Fig. 5), predicted potential PKs that are responsible for modifying the phosphorylation sites and performed an analysis of the phosphorylation conservation status across different organisms. We anticipated that such a database can be a useful resource for further analyses. The database will be continuously updated and maintained when new phosphorylation sites are reported.
Motif-based analysis of phospho-sites
In this study, we chose Motif-X (http://motif-x.med.harvard.edu/motif-x.html), a widely-used online tool for detecting phosphorylation motifs from the phosphoproteomic data45. For each species, known phosphorylated peptides in length of 13 with central characters of S/T/Y residues were prepared as the foreground data set, while non-phosphorylated peptides in the same proteins were regarded as the background data set. The default parameters were adopted, with a p-value <0.000001. The phosphorylation motifs were calculated for pS, pT and pY sites, respectively. The most significant motifs of the three types of residues for each species were diagrammed in sequence logos (Fig. 5).
Prediction of kinase-specific phosphorylation sites in the kinase family level
Previously, we developed a software package of GPS 2.116, in which the predictors were established based on the kinase classifications of mammalians. Thus, here we only predicted potentially kinase-specific phospho-sites in H. sapiens, M. musculus and R. norvegicuse, respectively. Also, because the prediction accuracy at the group level is limited, here we only predicted ssKSRs for known phosphorylation sites in the family level and further counted in the group level. Totally, 42 and 20 predictors were selected from GPS 2.1 for serine/threonine kinases and tyrosine kinases, separately. The high thresholds were adopted for the prediction.
The calculation of RCS and rRCS
We analyzed the conservation distributions of phosphorylation sites in H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae, separately. First, the InParanoid 4.1 program46 was obtained from Stockholm Bioinformatics Centre (http://InParanoid.sbc.su.se) for pairwisely detecting orthologs among the seven species. All orthologous proteins among different species were multi-aligned by Clustal Omega (http://www.clustal.org/omega/)47. As previously described43, for each species, if a serine, threonine, or tyrosine residue in a multiple sequence alignment (MSA) is phosphorylated, the corresponding column was regarded as the modified position. Other columns containing serines, threonines or tyrosines were taken as the reference positions. To calculate the conservation of a phosphorylated site from a MSA, we adopted a previously defined RCSp43, which can be calculated as below:
The Np/N was defined as the Residue Conservation Ratio (RCR)43. The N is the number of sequences with the maximum branch length (MBL), which is the maximum branch distance between any two species that contain a conserved phospho-site. The Np is the number of phospho-sites observed in the column. Then the calculated RCSp values of phosphorylated sites were mapped into the reference distribution to calculate the relative RCS or rRCS43. The species tree of the seven organisms was taken from the Interactive Tree Of Life (iTOL, http://itol.embl.de/)48.
How to cite this article: Ullah, S. et al. dbPAF: an integrative database of protein phosphorylation in animals and fungi. Sci. Rep. 6, 23534; doi: 10.1038/srep23534 (2016).
Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415–1426 (2007).
Jin, J. & Pawson, T. Modular evolution of phosphorylation-based signalling systems. Philos Trans R Soc Lond B Biol Sci 367, 2540–2555 (2012).
Seet, B. T., Dikic, I., Zhou, M. M. & Pawson, T. Reading protein modifications with interaction domains. Nat Rev Mol Cell Biol 7, 473–483 (2006).
Pan, Z. et al. dbPSP: a curated database for protein phosphorylation sites in prokaryotes. Database (Oxford) 2015, bav031 (2015).
Cheng, H. et al. dbPPT: a comprehensive database of protein phosphorylation in plants. Database (Oxford) 2014, bau121 (2014).
Li, L. et al. The human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer. Genome Res 22, 1222–1230 (2012).
Ubersax, J. A. & Ferrell, J. E. Jr. Mechanisms of specificity in protein phosphorylation. Nat Rev Mol Cell Biol 8, 530–541 (2007).
Faller, W. J. et al. mTORC1-mediated translational elongation limits intestinal tumour initiation and growth. Nature 517, 497–500 (2015).
Martin, I. et al. Ribosomal protein s15 phosphorylation mediates LRRK2 neurodegeneration in Parkinson’s disease. Cell 157, 472–485 (2014).
Lahiry, P., Torkamani, A., Schork, N. J. & Hegele, R. A. Kinase mutations in human disease: interpreting genotype-phenotype relationships. Nat Rev Genet 11, 60–74 (2010).
Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat Biotechnol 33, 990–995 (2015).
Zanivan, S. et al. In Vivo SILAC-Based Proteomics Reveals Phosphoproteome Changes during Mouse Skin Carcinogenesis. Cell Rep 3, 552–566 (2013).
Lundby, A. et al. Quantitative maps of protein phosphorylation sites across 14 different rat organs and tissues. Nat Commun 3, 876 (2012).
Horn, H. et al. KinomeXplorer: an integrated platform for kinome biology studies. Nat Methods 11, 603–604 (2014).
Miller, M. L. et al. Linear motif atlas for phosphorylation-dependent signaling. Sci Signal 1, ra2 (2008).
Xue, Y. et al. GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Mol Cell Proteomics 7, 1598–1608 (2008).
Qi, L. et al. Systematic analysis of the phosphoproteome and kinase-substrate networks in the mouse testis. Mol Cell Proteomics 13, 3626–3638 (2014).
Beltrao, P. et al. Systematic functional prioritization of protein posttranslational modifications. Cell 150, 413–425 (2012).
Wagih, O., Reimand, J. & Bader, G. D. MIMP: predicting the impact of mutations on kinase-substrate phosphorylation. Nat Methods 12, 531–533 (2015).
Wang, Y. et al. Reconfiguring phosphorylation signaling by genetic polymorphisms affects cancer susceptibility. J Mol Cell Biol 7, 187–202 (2015).
Blom, N., Kreegipuu, A. & Brunak, S. PhosphoBase: a database of phosphorylation sites. Nucleic Acids Res 26, 382–386 (1998).
Diella, F., Gould, C. M., Chica, C., Via, A. & Gibson, T. J. Phospho.ELM: a database of phosphorylation sites–update 2008. Nucleic Acids Res 36, D240–244 (2008).
Diella, F. et al. Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics 5, 79 (2004).
Lee, T. Y. et al. dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res 34, D622–627 (2006).
Olsen, J. V. et al. Global, in vivo and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006).
Gnad, F., Gunawardena, J. & Mann, M. PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res 39, D253–260 (2011).
Huang, K. Y. et al. dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res (2015).
Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43, D512–520 (2015).
Hornbeck, P. V., Chabra, I., Kornhauser, J. M., Skrzypek, E. & Zhang, B. PhosphoSite: A bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics 4, 1551–1561 (2004).
Bodenmiller, B. et al. PhosphoPep–a database of protein phosphorylation sites in model organisms. Nat Biotechnol 26, 1339–1340 (2008).
Bodenmiller, B. et al. PhosphoPep–a phosphoproteome resource for systems biology research in Drosophila Kc167 cells. Mol Syst Biol 3, 139 (2007).
Sadowski, I. et al. The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database (Oxford) 2013, bat026 (2013).
Stark, C. et al. PhosphoGRID: a database of experimentally verified in vivo protein phosphorylation sites from the budding yeast Saccharomyces cerevisiae. Database (Oxford) 2010, bap026 (2010).
Li, J. et al. SysPTM 2.0: an updated systematic resource for post-translational modification. Database (Oxford) 2014, bau025 (2014).
Li, H. et al. SysPTM: a systematic resource for proteomic research on post-translational modifications. Mol Cell Proteomics 8, 1839–1849 (2009).
Goel, R., Harsha, H. C., Pandey, A. & Prasad, T. S. Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. Mol Biosyst 8, 453–463 (2012).
UniProt: a hub for protein information. Nucleic Acids Res 43, D204–212 (2015).
Boratyn, G. M. et al. BLAST: a more efficient report with usability improvements. Nucleic Acids Res 41, W29–33 (2013).
Hunter, T. Tyrosine phosphorylation: thirty years and counting. Curr Opin Cell Biol 21, 140–146 (2009).
Wang, Y. et al. EKPD: a hierarchical database of eukaryotic protein kinases and protein phosphatases. Nucleic Acids Res 42, D496–502 (2014).
Lindberg, R. A., Quinn, A. M. & Hunter, T. Dual-specificity protein kinases: will any hydroxyl do? Trends Biochem Sci 17, 114–119 (1992).
Iakoucheva, L. M. et al. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res 32, 1037–1049 (2004).
Minguez, P. et al. Deciphering a global network of functionally associated post-translational modifications. Mol Syst Biol 8, 599 (2012).
van Wijk, K. J., Friso, G., Walther, D. & Schulze, W. X. Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs. Plant Cell 26, 2367–2389 (2014).
Schwartz, D. & Gygi, S. P. An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat Biotechnol 23, 1391–1398 (2005).
Sonnhammer, E. L. & Ostlund, G. InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Res 43, D234–239 (2015).
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7, 539 (2011).
Letunic, I. & Bork, P. Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res 39, W475–478 (2011).
This work was supported by grants from the National Basic Research Program (973 project) (2013CB933900, 2012CB910101 and 2011CB910600), Natural Science Foundation of China (31171263, 81272578, 31501069 and J1103514), International Science & Technology Cooperation Program of China (2014DFB30020) and China Postdoctoral Science Foundation (2014M550392).
The authors declare no competing financial interests.
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Ullah, S., Lin, S., Xu, Y. et al. dbPAF: an integrative database of protein phosphorylation in animals and fungi. Sci Rep 6, 23534 (2016). https://doi.org/10.1038/srep23534
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