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

NucPred

Fetching P08640 from www.uniprot.org...

The NucPred score for your sequence is 0.42 (see score help below)

   1  MQRPFLLAYLVLSLLFNSALGFPTALVPRGSSEGTSCNSIVNGCPNLDFN    50
  51  WHMDQQNIMQYTLDVTSVSWVQDNTYQITIHVKGKENIDLKYLWSLKIIG   100
 101  VTGPKGTVQLYGYNENTYLIDNPTDFTATFEVYATQDVNSCQVWMPNFQI   150
 151  QFEYLQGSAAQYASSWQWGTTSFDLSTGCNNYDNQGHSQTDFPGFYWNID   200
 201  CDNNCGGTKSSTTTSSTSESSTTTSSTSESSTTTSSTSESSTTTSSTSES   250
 251  STSSSTTAPATPTTTSCTKEKPTPPTTTSCTKEKPTPPHHDTTPCTKKKT   300
 301  TTSKTCTKKTTTPVPTPSSSTTESSSAPVPTPSSSTTESSSAPVTSSTTE   350
 351  SSSAPVPTPSSSTTESSSAPVTSSTTESSSAPVTSSTTESSSAPVPTPSS   400
 401  STTESSSAPVTSSTTESSSAPVTSSTTESSSAPVTSSTTESSSAPVTSST   450
 451  TESSSAPVPTPSSSTTESSSAPVTSSTTESSSAPVPTPSSSTTESSSAPV   500
 501  TSSTTESSSAPVPTPSSSTTESSSAPAPTPSSSTTESSSAPVTSSTTESS   550
 551  SAPVPTPSSSTTESSSTPVTSSTTESSSAPVPTPSSSTTESSSAPVPTPS   600
 601  SSTTESSSAPAPTPSSSTTESSSAPVTSSTTESSSAPVPTPSSSTTESSS   650
 651  APVPTPSSSTTESSSAPVPTPSSSTTESSSAPVTSSTTESSSAPVTSSTT   700
 701  ESSSAPVPTPSSSTTESSSAPVPTPSSSTTESSSAPVPTPSSSTTESSSA   750
 751  PVTSSTTESSSAPVPTPSSSTTESSSAPVPTPSSSTTESSSAPVPTPSSS   800
 801  TTESSVAPVPTPSSSSNITSSAPSSTPFSSSTESSSVPVPTPSSSTTESS   850
 851  SAPVSSSTTESSVAPVPTPSSSSNITSSAPSSIPFSSTTESFSTGTTVTP   900
 901  SSSKYPGSQTETSVSSTTETTIVPTKTTTSVTTPSTTTITTTVCSTGTNS   950
 951  AGETTSGCSPKTVTTTVPTTTTTSVTTSSTTTITTTVCSTGTNSAGETTS  1000
1001  GCSPKTITTTVPCSTSPSETASESTTTSPTTPVTTVVSTTVVTTEYSTST  1050
1051  KPGGEITTTFVTKNIPTTYLTTIAPTPSVTTVTNFTPTTITTTVCSTGTN  1100
1101  SAGETTSGCSPKTVTTTVPCSTGTGEYTTEATTLVTTAVTTTVVTTESST  1150
1151  GTNSAGKTTTGYTTKSVPTTYVTTLAPSAPVTPATNAVPTTITTTECSAA  1200
1201  TNAAGETTSVCSAKTIVSSASAGENTAPSATTPVTTAIPTTVITTESSVG  1250
1251  TNSAGETTTGYTTKSIPTTYITTLIPGSNGAKNYETVATATNPISIKTTS  1300
1301  QLATTASASSVAPVVTSPSLTGPLQSASGSAVATYSVPSISSTYQGAANI  1350
1351  KVLGNFMWLLLALPVVF                                   1367

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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