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

NucPred

Fetching P02467 from www.uniprot.org...

The NucPred score for your sequence is 0.20 (see score help below)

   1  MLSFVDTRILLLLAVTSYLATSQHLFQASAGRKGPRGDKGPQGERGPPGP    50
  51  PGRDGEDGPPGPPGPPGPPGLGGNFAAQYDPSKAADFGPGPMGLMGPRGP   100
 101  PGASGPPGPPGFQGVPGEPGEPGQTGPQGPRGPPGPPGKAGEDGHPGKPG   150
 151  RPGERGVAGPQGARGFPGTPGLPGFKGIRGHNGLDGQKGQPGTPGTKGEP   200
 201  GAPGENGTPGQPGARGLPGERGRIGAPGPAGARGSDGSAGPTGPAGPIGA   250
 251  AGPPGFPGAPGAKGEIGPAGNVGPTGPAGPRGEIGLPGSSGPVGPPGNPG   300
 301  ANGLPGAKGAAGLPGVAGAPGLPGPRGIPGPPGPAGPSGARGLVGEPGPA   350
 351  GAKGESGNKGEPGAAGPPGPPGPSGEEGKRGSNGEPGSAGPPGPAGLRGV   400
 401  PGSRGLPGADGRAGVMGPAGNRGASGPVGAKGPNGDAGRPGEPGLMGPRG   450
 451  LPGQPGSPGPAGKEGPVGFPGADGRVGPIGPAGNRGEPGNIGFPGPKGPT   500
 501  GEPGKPGEKGNVGLAGPRGAPGPEGNNGAQGPPGVTGNQGAKGETGPAGP   550
 551  PGFQGLPGPSGPAGEAGKPGERGLHGEFGVPGPAGPRGERGLPGESGAVG   600
 601  PAGPIGSRGPSGPPGPDGNKGEPGNVGPAGAPGPAGPGGIPGERGVAGVP   650
 651  GGKGEKGAPGLRGDTGATGRDGARGLPGAIGAPGPAGGAGDRGEGGPAGP   700
 701  AGPAGARGIPGERGEPGPVGPSGFAGPPGAAGQPGAKGERGPKGPKGETG   750
 751  PTGAIGPIGASGPPGPVGAAGPAGPRGDAGPPGMTGFPGAAGRVGPPGPA   800
 801  GITGPPGPPGPAGKDGPRGLRGDVGPVGRTGEQGIAGPPGFAGEKGPSGE   850
 851  AGAAGPPGTPGPQGILGAPGILGLPGSRGERGLPGIAGATGEPGPLGVSG   900
 901  PPGARGPSGPVGSPGPNGAPGEAGRDGNPGNDGPPGRDGAPGFKGERGAP   950
 951  GNPGPSGALGAPGPHGQVGPSGKPGNRGDPGPVGPVGPAGAFGPRGLAGP  1000
1001  QGPRGEKGEPGDKGHRGLPGLKGHNGLQGLPGLAGQHGDQGPPGNNGPAG  1050
1051  PRGPPGPSGPPGKDGRNGLPGPIGPAGVRGSHGSQGPAGPPGPPGPPGPP  1100
1101  GPNGGGYEVGFDAEYYRADQPSLRPKDYEVDATLKTLNNQIETLLTPEGS  1150
1151  KKNPARTCRDLRLSHPEWSSGFYWIDPNQGCTADAIRAYCDFATGETCIH  1200
1201  ASLEDIPTKTWYVSKNPKDKKHIWFGETINGGTQFEYNGEGVTTKDMATQ  1250
1251  LAFMRLLANHASQNITYHCKNSIAYMDEETGNLKKAVILQGSNDVELRAE  1300
1301  GNSRFTFSVLVDGCSKKNNKWGKTIIEYRTNKPSRLPILDIAPLDIGGAD  1350
1351  QEFGLHIGPVCFK                                       1363

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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