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

NucPred

Fetching P02466 from www.uniprot.org...

The NucPred score for your sequence is 0.25 (see score help below)

   1  MLSFVDTRTLLLLAVTSCLATCQSLQMGSVRKGPTGDRGPRGQRGPAGPR    50
  51  GRDGVDGPVGPPGPPGAPGPPGPPGPPGLTGNFAAQYSDKGVSAGPGPMG   100
 101  LMGPRGPPGAVGAPGPQGFQGPAGEPGEPGQTGPAGSRGPAGPPGKAGED   150
 151  GHPGKPGRPGERGVVGPQGARGFPGTPGLPGFKGIRGHNGLDGLKGQPGA   200
 201  QGVKGEPGAPGENGTPGQAGARGLPGERGRVGAPGPAGARGSDGSVGPVG   250
 251  PAGPIGSAGPPGFPGAPGPKGELGPVGNPGPAGPAGPRGEAGLPGLSGPV   300
 301  GPPGNPGANGLTGAKGATGLPGVAGAPGLPGPRGIPGPVGAAGATGPRGL   350
 351  VGEPGPAGSKGETGNKGEPGSAGAQGPPGPSGEEGKRGSPGEPGSAGPAG   400
 401  PPGLRGSPGSRGLPGADGRAGVMGPPGNRGSTGPAGVRGPNGDAGRPGEP   450
 451  GLMGPRGLPGSPGNVGPAGKEGPVGLPGIDGRPGPIGPAGPRGEAGNIGF   500
 501  PGPKGPSGDPGKPGEKGHPGLAGARGAPGPDGNNGAQGPPGPQGVQGGKG   550
 551  EQGPAGPPGFQGLPGPSGTAGEVGKPGERGLPGEFGLPGPAGPRGERGPP   600
 601  GESGAAGPSGPIGIRGPSGAPGPDGNKGEAGAVGAPGSAGASGPGGLPGE   650
 651  RGAAGIPGGKGEKGETGLRGEIGNPGRDGARGAPGAIGAPGPAGASGDRG   700
 701  EAGAAGPSGPAGPRGSPGERGEVGPAGPNGFAGPAGSAGQPGAKGEKGTK   750
 751  GPKGENGIVGPTGPVGAAGPSGPNGPPGPAGSRGDGGPPGMTGFPGAAGR   800
 801  TGPPGPSGITGPPGPPGAAGKEGIRGPRGDQGPVGRTGEIGASGPPGFAG   850
 851  EKGPSGEPGTTGPPGTAGPQGLLGAPGILGLPGSRGERGQPGIAGALGEP   900
 901  GPLGIAGPPGARGPPGAVGSPGVNGAPGEAGRDGNPGSDGPPGRDGQPGH   950
 951  KGERGYPGNIGPTGAAGAPGPHGSVGPAGKHGNRGEPGPAGSVGPVGAVG  1000
1001  PRGPSGPQGIRGDKGEPGDKGARGLPGLKGHNGLQGLPGLAGLHGDQGAP  1050
1051  GPVGPAGPRGPAGPSGPIGKDGRSGHPGPVGPAGVRGSQGSQGPAGPPGP  1100
1101  PGPPGPPGVSGGGYDFGFEGGFYRADQPRSQPSLRPKDYEVDATLKSLNN  1150
1151  QIETLLTPEGSRKNPARTCRDLRLSHPEWKSDYYWIDPNQGCTMDAIKVY  1200
1201  CDFSTGETCIQAQPVNTPAKNAYSRAQANKHVWLGETINGGSQFEYNAEG  1250
1251  VSSKEMATQLAFMRLLANRASQNITYHCKNSIAYLDEETGRLNKAVILQG  1300
1301  SNDVELVAEGNSRFTYTVLVDGCSKKTNEWDKTVIEYKTNKPSRLPFLDI  1350
1351  APLDIGGTNQEFRVEVGPVCFK                              1372

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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