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

NucPred

Fetching P47990 from www.uniprot.org...

The NucPred score for your sequence is 0.29 (see score help below)

   1  MAPPETGDELVFFVNGKKVVEKDVDPETTLLTYLRRKLGLCGTKLGCGEG    50
  51  GCGACTVMISKYDPFQKKILHHTANACLFPICALHHVAVTTVEGIGNTKS   100
 101  RLHPAQERIAKSHGSQCGFCTPGIVMSMYTLLRNKPKPKMEDIEDAFQGN   150
 151  LCRCTGYRPILEGYRTFAVDSNCCGKAANGTGCCHSKGENSMNGGCCGGK   200
 201  ANGPGCCMNEKENVTMMSSSLFDSSEFQPLDPTQEPIFPPELMTQRNKEQ   250
 251  KQVCFKGERVMWIQPTTLQELVALKSQYPNAKLVVGNTEVGIEMRLKNML   300
 301  YPVILAPAWIPEMNAVQQTETGITFGAACTLSSVEEVLRKAVAELPSYKT   350
 351  EIFQAALEQLRWFAGPQIRNVAALGGNIMTASPISDLNPVLMASGSKLTL   400
 401  ISMEGKRTVMMDEKFFTGYRKTIVKPEEVLLSVEIPYSKEGEYFSAFKQA   450
 451  YRREDDIAIVTCGMRVLFQHGTSRVQEVKLSYGGMAPTTILALKTCRELA   500
 501  GRDWNEKLLQDACRLLAGEMDLSPSAPGGMVEFRRTLTLSFFFKFYLTVL   550
 551  QKLSKDQNGPNNLCEPVPPNYISATELFHKDPIASTQLFQEVPRGQLVED   600
 601  TVGRPLVHLSAAKQACGEAVYCDDIPHYENELYLTLVTSTQAHAKILSID   650
 651  ASEAQSVPGFVCFVSAKDVPGSNITGIANDETVFAEDVVTCVGHIIGAVI   700
 701  ADTQEHSRRAAKAVKIKYEELKPIVTIQEAIEQQSFIKPIKRIKKGDVNK   750
 751  GFEESDHILEGEMHIGGQEHFYLETHCTLAVPKGEDGEMELFVSTQNLMK   800
 801  TQEFTASALGVPSNRIVVRVKRMGGGFGGKETRNTILTTVVAVAAFKTGR   850
 851  PVRCMLDRDEDMLISGGRHPFLGRYKVGFMKNGKIKSLEVSYYSNGGNSA   900
 901  DLSHGVMDRALLHLDNSYNIPNVSIMGFICKTNLSSNTAFRGFGGPQGMM   950
 951  IAECWMSDLARKCGLPPEEVRKINLYHEGDLTHFNQKLEGFTLRRCWDEC  1000
1001  LSSSNYHARKKLIEEFNKQNRWKKRGMCIIPTKFGISFTVPFLNQAGALV  1050
1051  HVYTDGSVLLTHGGTEMGQGLHTKMIQVASRSLGIPTSKIYISETSTNTV  1100
1101  PNTSPTAASVSADINGMAVHNACQTILKRLEPIKQSNLKGSWEDWIKTAY  1150
1151  ENCISLSATGFYRIPDVGYNFETNKGKPFHYFSYGVACSEVEIDCLTGDH  1200
1201  KNIRTDIVMDVGTSLNPAIDIGQIEGAFVQGIGLFTMEELRYSPEGNLYT  1250
1251  RGPGMYKIPAFGDIPTEFYVSLLRDCPNSKAIYSSKAVGEPPLFLSASVF  1300
1301  YAIKDAIYSAREDSGVTEPFRLDSPATPERIRNACVDTFTKMCPSAEPGT  1350
1351  FKPWSVRA                                            1358

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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