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

NucPred

Fetching P56764 from www.uniprot.org...

The NucPred score for your sequence is 0.86 (see score help below)

   1  MAERANLVFHNKVIDGTAIKRLISRLIDHFGMAYTSHILDQVKTLGFQQA    50
  51  TATSISLGIDDLLTIPSKGWLVQDAEQQSWILEKHHHYGNVHAVEKLRQS   100
 101  IEIWYATSEYLRQEMNPNFRMTDPFNPVHMMSFSGARGNASQVHQLVGMR   150
 151  GLMSDPQGQMIDLPIQSNLREGLSLTEYIISCYGARKGVVDTAVRTSDAG   200
 201  YLTRRLVEVVQHIVVRRTDCGTIRGISVSPRNKNRMMSERIFIQTLIGRV   250
 251  LADDIYIGSRCVAFRNQDLGIGLVNRLITFGTQSISIRTPFTCRSTSWIC   300
 301  RLCYGRSPTHGDLVELGEAVGIIAGQSIGEPGTQLTLRTFHTGGVFTGGT   350
 351  AEHVRAPYNGKIKFNEDLVHPTRTRHGHPAFLCYIDLSVIIESEDIIHSV   400
 401  TIPPKSFLLVQNDQYVESEQVIAEIREGTYTFHFKERVRKYIYSDSEGEM   450
 451  HWSTDVSHAPEFTYSNVHLLPKTSHLWILSGGSCGSSLIRFSIHKDQDQM   500
 501  NIPFLSAERKSISSLSVNNDQVSQKFFSSDFADPKKLGIYDYSELNGNLG   550
 551  TSHYNLIYSAIFHENSDLLAKRRRNRFLIPFQSIQEQEKEFIPQSGISVE   600
 601  IPINGIFRRNSIFAFFDDPRYRRKSSGILKYGTLKADSIIQKEDMIEYRG   650
 651  VQKIKTKYEMKVDRFFFIPEEVHILPESSAIMVQNYSIIGVDTRLTLNIR   700
 701  SQVGGLIRVEKKKKRIELKIFSGDIHFPDKTDKISRHSGILIPPGRGKKN   750
 751  SKESKKFKNWIYVQRITPTKKKFFVLVRPVATYEIADSINLATLFPQDLF   800
 801  REKDNIQLRVFNYILYGNGKPTRGISDTSIQLVRTCLVLNWDKNSSLEEV   850
 851  RAFFVEVSTKGLIQDFIRIGLVKSHISYIRKRNNSPDSGLISADHMNPFY   900
 901  SISPKSGILQQSLRQNHGTIRMFLNRNKESQSLLILSSSNCFRMGPFNHV   950
 951  KHHNVINQSIKKNTLITIKNSSGPLGTATPISNFYSFLPLLTYNQISLIK  1000
1001  YFQLDNLKYIFQKINSYLIDENGIILNLDPYSNVVLNPFKLNWYFLHQNY  1050
1051  HHNYCEETSTIISLGQFFCENVCIAKKEPHLKSGQVLIVQRDSAVIRSAK  1100
1101  PYLATPGAKVHGHYSEILYEGDTLVTFIYEKSRSGDITQGLPKVEQVLEV  1150
1151  RSIDSISLNLEKRIKGWNKCITRILGIPWGFLIGAELTIVQSRISLVNKI  1200
1201  QKVYRSQGVQIHNRHIEIIVRQITSKVLVSEEGMSNVFLPGELIGLLRAE  1250
1251  RTGRALEEAICYRAVLLGITRASLNTQSFISEASFQETARVLAKAALRGR  1300
1301  IDWLKGLKENVVLGGVIPAGTGFNKGLVHCSRQHTNIILEKKTKNLALFE  1350
1351  GDMRDILFYHREFCDSSISKSDFSRI                          1376

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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