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

NucPred

Fetching Q6P158 from www.uniprot.org...

The NucPred score for your sequence is 0.79 (see score help below)

   1  MSSSVRRKGKPGKGGGKGSSRGGRGGRSHASKSHGSGGGGGGGGGGGGGN    50
  51  RKASSRIWDDGDDFCIFSESRRPSRPSNSNISKGESRPKWKPKAKVPLQT   100
 101  LHMTSENQEKVKALLRDLQEQDADAGSERGLSGEEEDDEPDCCNDERYWP   150
 151  AGQEPSLVPDLDPLEYAGLASVEPYVPEFTVSPFAVQKLSRYGFNTERCQ   200
 201  AVLRMCDGDVGASLEHLLTQCFSETFGERMKISEAVNQISLDECMEQRQE   250
 251  EAFALKSICGEKFIERIQNRVWTIGLELEYLTSRFRKSKPKESTKNVQEN   300
 301  SLEICKFYLKGNCKFGSKCRFKHEVPPNQIVGRIERSVDDSHLNAIEDAS   350
 351  FLYELEIRFSKDHKYPYQAPLVAFYSTNENLPLACRLHISEFLYDKALTF   400
 401  AETSEPVVYSLITLLEEESEIVKLLTNTHHKYSDPPVNFLPVPSRTRINN   450
 451  PACHKTVIPNNSFVSNQIPEVEKASESEESDEDDGPAPVIVENESYVNLK   500
 501  KKISKRYDWQAKSVHAENGKICKQFRMKQASRQFQSILQERQSLPAWEER   550
 551  ETILNLLRKHQVVVISGMTGCGKTTQIPQFILDDSLNGPPEKVANIICTQ   600
 601  PRRISAISVAERVAKERAERVGLTVGYQIRLESVKSSATRLLYCTTGVLL   650
 651  RRLEGDTALQGVSHIIVDEVHERTEESDFLLLVLKDIVSQRPGLQVILMS   700
 701  ATLNAELFSDYFNSCPVITIPGRTFPVDQFFLEDAIAVTRYVLQDGSPYM   750
 751  RSMKQISKEKLKARRNRTAFEEVEEDLRLSLHLQDQDSVKDAVPDQQLDF   800
 801  KQLLARYKGVSKSVIKTMSIMDFEKVNLELIEALLEWIVDGKHSYPPGAI   850
 851  LVFLPGLAEIKMLYEQLQSNSLFNNRRSNRCVIHPLHSSLSSEEQQAVFV   900
 901  KPPAGVTKIIISTNIAETSITIDDVVYVIDSGKMKEKRYDASKGMESLED   950
 951  TFVSQANALQRKGRAGRVASGVCFHLFTSHHYNHQLLKQQLPEIQRVPLE  1000
1001  QLCLRIKILEMFSAHNLQSVFSRLIEPPHTDSLRASKIRLRDLGALTPDE  1050
1051  RLTPLGYHLASLPVDVRIGKLMLFGSIFRCLDPALTIAASLAFKSPFVSP  1100
1101  WDKKEEANQKKLEFAFANSDYLALLQAYKGWQLSTKEGVRASYNYCRQNF  1150
1151  LSGRVLQEMASLKRQFTELLSDIGFAREGLRAREIEKRAQGGDGVLDATG  1200
1201  EEANSNAENPKLISAMLCAALYPNVVQVKSPEGKFQKTSTGAVRMQPKSA  1250
1251  ELKFVTKNDGYVHIHPSSVNYQVRHFDSPYLLYHEKIKTSRVFIRDCSMV  1300
1301  SVYPLVLFGGGQVNVQLQRGEFVVSLDDGWIRFVAASHQVAELVKELRCE  1350
1351  LDQLLQDKIKNPSIDLCTCPRGSRIISTIVKLVTTQ                1386

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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