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

NucPred

Fetching P51830 from www.uniprot.org...

The NucPred score for your sequence is 0.53 (see score help below)

   1  MASSPHQQLLHHHSTEVSCDSSGDSNSVRVKINPKQLSSNTHPKHCKYSI    50
  51  SSSCSSSGDSGGLPRRVGGGGRLRRQKKLPQLFERASSRWWDPKFDSMNL   100
 101  EEACLERCFPQTQRRFRYALFYVGFACLLWSIYFAVHMKSKVIVMVVPAL   150
 151  CFLVVCVGFFLFTFTKLYARHYAWTSLALTLLVFALTLAAQFQVWTPLSG   200
 201  RVDSSNHTLTATPADTCLSQVGSFSICIEVLLLLYTVMQLPLYLSLFLGV   250
 251  VYSVLFETFGYHFRNEDCYPSPGPGALHWELLSRALLHVCIHAIGIHLFV   300
 301  MSQVRSRSTFLKVGQSIMHGKDLEVEKALKERMIHSVMPRIIADDLMKQG   350
 351  DEESENSVKRHATSSPKNRKKKSSIQKAPIAFRPFKMQQIEEVSILFADI   400
 401  VGFTKMSANKSAHALVGLLNDLFGRFDRLCEQTKCEKISTLGDCYYCVAG   450
 451  CPEPRADHAYCCIEMGLGMIKAIEQFCQEKKEMVNMRVGVHTGTVLCGIL   500
 501  GMRRFKFDVWSNDVNLANLMEQLGVAGKVHISEATAKYLDDRYEMEDGRV   550
 551  IERLGQSVVADQLKGLKTYLISGQRAKESHCSCAEALLSGFEVIDDSRES   600
 601  SGPRGQGTASPGSVSDLAQTVKTFDNLKTCPSCGITFAPKSEAGAEGGTV   650
 651  QNGCQDEPKTSTKASGGPNSKTQNGLLSPPAEEKLTNSQTSLCEILQEKG   700
 701  RWAGVSLDQSALLPLRFKNIREKTDAHFVDVIKEDSLMKDYFFKPPINQF   750
 751  SLNFLDQELERSYRTSYQEEVIKNSPVKTFASATFSSLLDVFLSTTVFLI   800
 801  LSITCFLKYGATATPPPPAALAVFGADLLLEVLSLIVSIRMVFFLEDVMT   850
 851  CTKWLLEWIAGWLPRHCIGAILVSLPALAVYSHITSEFETNIHVTMFTGS   900
 901  AVLVAVVHYCNFCQLSSWMRSSLATIVGAGLLLLLHISLCQDSSIVMSPL   950
 951  DSAQNFSAQRNPCNSSVLQDGRRPASLIGKELILTFFLLLLLVWFLNREF  1000
1001  EVSYRLHYHGDVEADLHRTKIQSMRDQADWLLRNIIPYHVAEQLKVSQTY  1050
1051  SKNHDSGGVIFASIVNFSEFYEENYEGGKECYRVLNELIGDFDELLSKPD  1100
1101  YNSIEKIKTIGATYMAASGLNTAQCQEGGHPQEHLRILFEFAKEMMRVVD  1150
1151  DFNNNMLWFNFKLRVGFNHGPLTAGVIGTTKLLYDIWGDTVNIASRMDTT  1200
1201  GVECRIQVSEESYRVLSKMGYDFDYRGTVNVKGKGQMKTYLYPKCTDNGV  1250
1251  VPQHQLSISPDIRVQVDGSIGRSPTDEIANLVPSVQYSDKASLGSDDSTQ  1300
1301  AKEARLSSKRSWREPVKAEERFPFGKAIEKDSCEDIGVEEASELSKLNVS  1350
1351  KSV                                                 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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