PTM prediction tools

Resource	Classification	Reference	Information/
	method/Database		PMID
General PTM-related
ELM	Consensus	1	Predicts Eukaryotic Linear Motifs (ELMs) based on
	patterns		consensus patterns. Applies context-based rules
			and logical filters. PMID:12824381
PROSITE	Consensus	2	Curated database of consensus patterns for many
	patterns		types of PTMs, including phosphorylation sites.
			MotifScan feature allows for scanning of query
			sequence. PMID:12230035
HPRD	Database	3	Human Protein Reference Database (HPRD).
			Highly curated database of disease-related
			proteins and their PTMs. PMID:14681466
RESID	Database	4	Part of the PIR protein database. Comprehensive
			collection of annotations and structures for PTMs.
			PMID:12520062
Phosphorylation
Scansite	Weight matrix	5	Based on peptide library studies. Predicts kinase-
			specific motifs and other types of motifs involved
			in signal transduction, e.g. SH2 domain binding.
			PMID:11283593
PREDIKIN	Expert system	6	The program produces a prediction of substrates
			for S/T protein kinases based on the primary
			sequence of a protein kinase catalytic domain.
			PMID:12502784
NetPhos	Neural network	7	Predicts general phosphorylation status based on
			sets of experimentally validated S, T and Y
			phosphorylation sites. PMID:10600390
NetPhosK	Neural network	8	Predicts kinase-specific phosphorylation sites
			based on sets of experimentally validated S, T
			and Y phosphorylation sites. PMID:15174133
PhosphoELM	Database	9	Curated database of validated phosphorylation
			sites. PMID:9847189
Phosphosite	Database		Curated database of in vivo validated phospho-
			rylation sites.
Glycosylation
bigPI	Weight matrix	10	Predicts GPI-modification sites using a composite
			prediction function including a weight matrix
			and physical model. PMID:10497036
DGPI	Weight matrix		Predics GPI cleavage site (GPI-anchor)
			in a protein.
GlycoMod	Look-up table	11	Predicts glycan structure from its experimentally
			determined mass. PMID:11680880
NetOGlyc	Neural network	12	Predicts mucin type CalNac O-glycosylation sites
			in mammalian proteins. PMID:15385431
NetNGlyc	Neural network	13	Predicts N-glycosylation sites in human proteins
			by examining the sequence context of Asn-
			Xaa-Ser/Thr sequons. In preparation.
DictyOGlyc	Neural network	14	Predicts GlcNAc o-glycosylation sites in Dictyoste-
			lium discoideum proteins. PMID:10521537
YinOYang	Neural network		Predicts O-beta-GlcNAc attachment sites in eukaryotic
			protein sequences.
O-GlycBase	Database	15	Predicts O-beta-GlcNAc attachment sites in eukaryotic
			protein sequences. PMID:9847232
Sulfation
Sulfinator	HMM	16	Predicts tyrosine sulfation sites using a combination
			of HMM models. PMID:12050077
Cleavage
ProP	Neural network	17	Predicts arginine and lysine propeptide cleavage sites
			in protein sequences. PMID:14985543
NetPicoRNA	Neural network	18	Predicts picornaviral protease cleavage sites in
			protein sequences. PMID:8931139
NetCorona	Neural network	19	Predicts coronavirus 3C-like proteinase (or protease)
			cleavage sites on protein sequences. PMID:15180906
Myristoylation
Myristoylator	Neural network	20	Predicts N-terminal myristoylation on protein sequences.
			PMID:15174132
NMT	Weight matrix	21	Predicts myristoylation sites on protein sequences.
			PMID:11955008
Sumoylation
SUMOplot	Weight matrix		Prediction of sumoylation on protein sequences.

Part of the table has been taken from the review:

"Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence"
Blom, N., Sicheritz-Pontén, T., Gupta, R., Gammeltoft, S. and Brunak, S.
Proteomics 2004, 4, 1633-1649. PMID:15174133


References
1. Puntervoll, P., Linding, R., Gemund, C., Chabanis-Davidson, S. et al., Nucleic Acids Res. 2003, 31, 3625-3630.
2. Sigrist, C., Cerutti, L., Hulo, N., Gattiker, A. et al., Brief Bioinform. 2002, 3, 265-274.
3. Peri, S., Navarro, J., Amanchy, R., Kristiansen, T. et al., Genome Res. 2003, 13, 2363-2371.
4. Garavelli, J., Nucleic Acids Res. 2003, 31, 499-501.
5. Yaffe, M., Leparc, G., Lai, J., Obata, T. et al., Nat. Biotechnol.2001, 19, 348-353.
6. Brinkworth, R., Breini, R., Kobe, B., Proc. Natl. Acad. USA 2003, 100, 74-79.
7. Blom, N., Gammeltoft, S., Brunak, S., J. Mol. Biol. 1999, 294, 1351-1362.
8. Blom, N., Sicheritz-Pontén, T., Gupta, R.,Gammeltoft, S. et al., Proteomics 2004, 4, 1633-1649.
9. Kreegipuu, A., Blom, N., Brunak, S., Nucleic Acids Res.1999, 27, 237-239.
10. Eisenhaber, B., Bork, P., Eisenhaber, F., J. Mol. Biol. 1999, 292, 741-758.
11. Cooper, C., Gasteiger, E., Packer, N., Proteomics 2001, 1, 340-349.
12. Julenius, K., Mølgaard, A., Gupta, R., Brunak, S., Glycobiology 2005, 15, 153-164.
13. Gupta, R., Jung, E., Brunak, S., In preparation, 2004.
14. Gupta, R., Jung, E., Gooley, A., Williams, K. et al., Glycobiology 1999, 9, 1009-1022.
15. Gupta, R., Birch, H., Rapacki, K., Brunak, S., Hansen, J., Nucleic Acids Res. 1999, 27, 365-370.
16. Monigatti, F., Gasteiger, E., Bairoch, A., Jung, E., Bioinformatics 2002, 18, 769-770.
17. Duckert, P., Brunak, S., Blom, N., Protein Eng. 2004, 17, 107-112.
18. Blom, N., Hansen, J., Blaas, D., Brunak, S., Protein Science, 1996, 5, 2203-2216.
19. Kiemer, L., Lund, O., Brunak, S., Blom, N., BMC Bioinformatics, 2004, 5, 72.
20. Bologna, G., Yvon, S., Veuthey, A.L., Proteomics, 2004, 4, 1626-1632.
21. Maurer-Stroh, S., Eisenhaber, B., Eisenhaber, F., J Mol Biol, 2002, 317, 541-557.



This page is part of the BioSapiens Network funded by the European Commission within its FP6 programme, under the thematic area "Life science, genomics and biotechnology for health", contract number LHSG-CT-2003-503265.

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