SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q90243 from www.uniprot.org...

The NucPred score for your sequence is 0.84 (see score help below)

   1  LTIALVGSQQTKYEPSFSGSKTYQYKYEGVILTGLPEKGLARAGLKVHCK    50
  51  VEISEVAQKTYLLKILNPEIQEYNGIWPKAPFYPASKLTQALASQLTQPI   100
 101  KFQYRNGQVGDIFASEDVSDTVLNIQRGILNMLQLTIKTTQNVYGLQENG   150
 151  IAGICEASYVIQEDRKANKIIVTKSKDLNNCNEKIKMDIGMAYSHTCSNC   200
 201  RKIRKNTRGTAAYTYILKPTDTGTLITQATSQEVHQLTPFNEMTGAAITE   250
 251  ARQKLVLEDAKVIHVTVPEQELKNRGSIQYQFASEILQTPIQLFKTRSPE   300
 301  TKIKEVLQHLVQNNQQQVQSDAPSKFLQLTQLLRACTHENIEGIWRQYEK   350
 351  TQLYRRWILDALPAAATPTAFRFITQRIMKRDLTDAEAIQTLVTAMHLVQ   400
 401  TNHQIVQMAAELVFDRANLKCPVLRKHAVLAYGSMVNRYCAETLNCREEA   450
 451  LKPLHDFANDAISRAHEEETVLALKALGNAGQPSSIKRIQKCLPGFSSGA   500
 501  SQLPVKIQVDAVMALRNIAKKEPGKVQELTMQLFMDHQLHSEVRMVASMV   550
 551  LLETRPSMALVATLAEALLKETSLQVASFTYSHMKAITRSTAPENHALSS   600
 601  ACNVAVKLLSRKLDRLSYRYSKAMHMDTFKYPLMAGAAANIHIINNAASI   650
 651  LPSAVVMKFQAYILSATADPLEIGLHTEGLQEVLMQNHEHIDQMPSAGKI   700
 701  QQIMKMLSGWKSVPSEKTLASAYIKLFGQEISFSRLDKKTIQEALQAVRE   750
 751  PVERQTVIKRVVNQLERGAAAQLSKPLLVAEVRRILPTCIGLPMEMSLYV   800
 801  SAVTTADINVQAHITPSPTNDFNVAQLLNSNIVLHTDVTPSIAMHTIAVM   850
 851  GINTHVIQTGVELHVKARTTVPMKFTAKIDLKEKNFKIESEPCQQETEVL   900
 901  SLSAQAFAISRNVEDLDAAKKNPLLPEEAVRNILNEQFNSGTEDSNERER   950
 951  AGKFARPSAEMMSQELMNSGEHQNRKGAHATRSACAKAKNFGFEVCFEGK  1000
1001  SENVAFLRDSPLYKIIGQHHCKIALKPSHSSEATIEKIQLELQTGNKAAS  1050
1051  KIIRVVAMQSLAEADEMKGNILKKLNKLLTVDGETQDSTLRGFKRRSSSS  1100
1101  SSSSSSSSSSSSSSSSSSSQQSRMEKRMEQDKLTENLERDRDHMRGKQSK  1150
1151  NKKQEWKNKQKKHHKQLPSSSSSSSSSSSGSNSSSSSSSSSSSSSRSHNH  1200
1201  RNNTRTLSKSKRYQNNNNSSSSSGSSSSSEEIQKNPEIFAYRFRSHRDKL  1250
1251  GFQNKRGRMSSSSSSSSSSSSQSTLNSKQDAKFLGDSSPPIFAFVARAVR  1300
1301  SDGLQQGYQVAAYTDNRVSRPRVQLLATEIIEKSRWQICADAILASNYKA  1350
1351  MALMRWGEECQDYKVAVSAVTGRLASHPSLQIKAKWSRIPRAAKQTQNIL  1400
1401  AEYVPGAAFMLGFSQKEQRNPSKQFKIILAVTSPNTIDTLIKAPKITLFK  1450
1451  QAVQIPVQIPMEPSDAERRSPGLASIMNEIPFLIEEATKSKCVAQENKFI  1500
1501  TFDGVKFSYQMPGGCYHILAQDCRSKVRFMVMLKQASMSKNLRAVNAKIY  1550
1551  NKDIDILPTTKGSVRLLINNNEIPLSQLPFTDSSGNIHIKRADEGVSVSA  1600
1601  QQYGLESLYFDGKTVQVKVTSEMRGKTCGLCGHNDGERRKEFRMPDGRQA  1650
1651  RGPSVSPTPGLCLEKTATEAASFCVIM                         1677

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

What does the NucPred score mean?

You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper.

NucPred score threshold Specificity Sensitivity
see above fraction of proteins predicted to be nuclear that actually are nuclear fraction of true nuclear proteins that are predicted (coverage)
0.10 0.45 0.88
0.20 0.52 0.83
0.30 0.57 0.77
0.40 0.63 0.69
0.50 0.70 0.62
0.60 0.71 0.53
0.70 0.81 0.44
0.80 0.84 0.32
0.90 0.88 0.21
1.00 1.00 0.02

Sequences which score >= 0.8 with NucPred and which are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.)

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