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

NucPred

Fetching Q02331 from www.uniprot.org...

The NucPred score for your sequence is 0.90 (see score help below)

   1  MSFFYSLFAPLIFLVTFIYNHVLLILFTVCIIGAAAFFVSYYLFGYSNTP    50
  51  SSASSSATPSSRNSPNKERSKKVPTILETDNEDDEIRVNGSPKSGTPTNT   100
 101  QTIEPPTSLNLNMVNSASGSNLSGARRMRKRDWAKKLYKSLVQDSPGRTP   150
 151  TDESSDEENANVVMGGGSVPRRRSKHGNSSRRRQSTAFQLAKDLIRRGSR   200
 201  SYFRQNQENNKDTRVRPPQEFFEPTDLPEIPQNLQPEIFYILHNLKMLEL   250
 251  PSEWKLDPREIEVRSFQAGDYIVKPGESDDAIYVAIDGELTVHIRHMEGK   300
 301  EYLVKCIPAGGSFFSLLSMLDILMDFPSIFRTVALKAADPCRVAKFPITS   350
 351  FRESYHKNPEAWIRPIQIVITRLLHVTLTTLHQYMGLSSELMRRRRDDDR   400
 401  HRNGSSTSKLQLSLSLKKKEKPFNINDNEQDQLIVARKWMAEAFGLVADD   450
 451  VVSEQIGSKIHLESYEAGHVIIEQGAEEEVLMMVLHGNLILAQYGSDFFA   500
 501  QIERESLFDEENNEEDESAVIRVTARELVGGLQILTNEPSFYTIRAAVQT   550
 551  RVAIMKKKDFSAFLEAHPEIYLPVAHSVLRRLSPFLRGVDFALDWVLVDS   600
 601  GHACYRAGDMADSLFVVLSGRLRSVEKKTVVEEFGRGDVLGMMEVLTKKP   650
 651  RATTVLAVRFSQLARVPEGLLNFIKMQYPQVGFRLVQLLGQYYSQTNRRA   700
 701  PFAAPTVIRTNELGATDPMSHIKNLHTIAVVPASPDVPLVPFTCELYHAL   750
 751  SSNLRVLRLSSQKVAACLDPSVLEKQADFRLMHWLNVQEDTYPLVIYECD   800
 801  FTQTNWTRRCLRQADAILVVAIGGKTPEKQTLMRELMNMNQDGVRTNKEL   850
 851  ILLWPESTKTPSGTIEWLKNSYFSGHHHIRAPKRMLQWNRKFRKMSRAEV   900
 901  MTPTSVENEVIEYYEKNVFWTPDRRSDFSRLARILTGNAIGLVLGGGGAR   950
 951  GAAHVGVLRALREEGIPVDIVGGTSIGSLIGGLYAETPDDVVVETRAASW  1000
1001  FNGMSSLWRKLLDLTYAHSAMFTGAQFNFSIKDLFEERLIEDLWISYFCI  1050
1051  STDISTSEMRVHRSGPLWAYCRASMSLAGYLPPLCDPQDGHLLLDGGYVN  1100
1101  NVPADVMRNLGARCVIACDVGSIEETNLYDYGDSLSGMWVLLKRLNPFGT  1150
1151  PPRILNMEEIQSRLAYVSCVRQLEVVKKASYCRYLRPPIEPFKTLDFPKF  1200
1201  QEIMELGLKYGREACHELITNDRAGILGDEKETRKFKRQQSRREKPDVSR  1250
1251  AVSFTDLAAAMSKIPVVRPTLRHSMSLNPSANGPVGRAGDHFLLDDDLFN  1300
1301  DTDYYEEESSQSENGNESFTEDEDLVIGPPSSSSSGGNVSENPRGTTPRP  1350
1351  PSS                                                 1353

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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