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

NucPred

Fetching O46392 from www.uniprot.org...

The NucPred score for your sequence is 0.23 (see score help below)

   1  MLSFVDTRTLLLLAVTSCLATCQSLQEATARKGPTGDRGPRGERGPPGPP    50
  51  GRDGDDGIPGPPGPPGPPGPPGLGGNFAAQYDGKGVGLGPGPMGLMGPRG   100
 101  PPGASGAPGPQGFQGPAGEPGEPGQTGPAGARGPPGPPGKAGEDGHPGKP   150
 151  GRPGERGVVGPQGARGFPGTPGLPGFKGIRGHNGLDGLKGQPGAPGVKGE   200
 201  PGAPGENGTPGQTGARGLPGERGRVGAPGPAGARGSDGSVGPVGPAGPIG   250
 251  SAGPPGFPGAPGPKGEIGPVGNPGPAGPAGPRGEVGLPGVSGPVGPPGNP   300
 301  GANGLTGAKGAAGLPGVAGAPGLPGPRGIPGPVGAAGATGARGIVGEPGP   350
 351  AGSKGESGNKGEPGSAGAQGPPGPSGEEGKRGPNGEAGSAGPSGPPGLRG   400
 401  SPGSRGLPGADGPAGVMGPPGPRGATGPAGVRGPNGDSGRPGEPGLMGPR   450
 451  GFPGAPGNVGPAGKEGPMGLPGIDGRPGPIGPAGARGEPGNIGFPGPKGP   500
 501  TGDPGKNGDKGHAGLAGARGAPGPDGNNGAQGPPGPQGVQGGKGEQGPAG   550
 551  PPGFQGLPGPAGTAGEVGKPGERGLPGEFGLPGPAGPRGERGPPGESGAA   600
 601  GPSGPIGSRGPSGPPGPDGNKGEPGVLGAPGTAGASGPGGLPGERGAAGI   650
 651  PGGKGEKGETGLRGEIGNPGRDGARGAPGAMGAPGPAGATGDRGEAGPAG   700
 701  PAGPAGPRGTPGERGEVGPAGPNGFAGPAGAAGQPGAKGERGTKGPKGEN   750
 751  GPVGPTGPIGSAGPSGPNGPPGPAGSRGDGGPPGATGFPGAAGRTGPPGP   800
 801  SGITGPPGPPGAAGKEGLRGPRGDQGPVGRTGETGASGPPGFTGEKGPSG   850
 851  EPGTAGPPGTPGPQGLLGAPGILGLPGSRGERGLPGVAGSVGEPGPLGIA   900
 901  GPPGARGPPGAVGAPGVNGAPGEAGRDGNPGNDGPPGRDGQAGHKGERGY   950
 951  PGNIGPVGAVGAPGPHGPVGPTGKHGNRGEPGPAGSVGPVGAVGPRGPSG  1000
1001  PQGIRGDKGEPGEKGPRGLPGLKGHNGLQGLPGLAGQHGDQGAPGSVGPA  1050
1051  GPRGPAGPSGPAGKDGRTGQPGTVGPAGIRGSQGSQGPAGPPGPPGPPGP  1100
1101  PGPSGGGYDFGYEGDFYRADQPRSPPSLRPKDYEVDATLKSLNNQIETLL  1150
1151  TPEGSRKNPARTCRDLRLSHPEWSSGYYWIDPNQGCTMDAIKVYCDFSTG  1200
1201  ETCIRAQPENIPAKNWYRNSKVKKHIWLGETINGGTQFEYNVEGVTTKEM  1250
1251  ATQLAFMRLLANHASQNITYHCKNSIAYMDEETGNLKKAVILQGSNDVEL  1300
1301  VAEGNSRFTYTVLVDGCSKKTNEWRKTIIEYKTNKPSRLPILDIAPLDIG  1350
1351  DADQEFRVDVGPVCFK                                    1366

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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