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

NucPred

Fetching Q15262 from www.uniprot.org...

The NucPred score for your sequence is 0.52 (see score help below)

   1  MDTTAAAALPAFVALLLLSPWPLLGSAQGQFSAGGCTFDDGPGACDYHQD    50
  51  LYDDFEWVHVSAQEPHYLPPEMPQGSYMIVDSSDHDPGEKARLQLPTMKE   100
 101  NDTHCIDFSYLLYSQKGLNPGTLNILVRVNKGPLANPIWNVTGFTGRDWL   150
 151  RAELAVSTFWPNEYQVIFEAEVSGGRSGYIAIDDIQVLSYPCDKSPHFLR   200
 201  LGDVEVNAGQNATFQCIATGRDAVHNKLWLQRRNGEDIPVAQTKNINHRR   250
 251  FAASFRLQEVTKTDQDLYRCVTQSERGSGVSNFAQLIVREPPRPIAPPQL   300
 301  LGVGPTYLLIQLNANSIIGDGPIILKEVEYRMTSGSWTETHAVNAPTYKL   350
 351  WHLDPDTEYEIRVLLTRPGEGGTGLPGPPLITRTKCAEPMRTPKTLKIAE   400
 401  IQARRIAVDWESLGYNITRCHTFNVTICYHYFRGHNESKADCLDMDPKAP   450
 451  QHVVNHLPPYTNVSLKMILTNPEGRKESEETIIQTDEDVPGPVPVKSLQG   500
 501  TSFENKIFLNWKEPLDPNGIITQYEISYSSIRSFDPAVPVAGPPQTVSNL   550
 551  WNSTHHVFMHLHPGTTYQFFIRASTVKGFGPATAINVTTNISAPTLPDYE   600
 601  GVDASLNETATTITVLLRPAQAKGAPISAYQIVVEELHPHRTKREAGAME   650
 651  CYQVPVTYQNAMSGGAPYYFAAELPPGNLPEPAPFTVGDNRTYQGFWNPP   700
 701  LAPRKGYNIYFQAMSSVEKETKTQCVRIATKAATEEPEVIPDPAKQTDRV   750
 751  VKIAGISAGILVFILLLLVVILIVKKSKLAKKRKDAMGNTRQEMTHMVNA   800
 801  MDRSYADQSTLHAEDPLSITFMDQHNFSPRYENHSATAESSRLLDVPRYL   850
 851  CEGTESPYQTGQLHPAIRVADLLQHINLMKTSDSYGFKEEYESFFEGQSA   900
 901  SWDVAKKDQNRAKNRYGNIIAYDHSRVILQPVEDDPSSDYINANYIDGYQ   950
 951  RPSHYIATQGPVHETVYDFWRMIWQEQSACIVMVTNLVEVGRVKCYKYWP  1000
1001  DDTEVYGDFKVTCVEMEPLAEYVVRTFTLERRGYNEIREVKQFHFTGWPD  1050
1051  HGVPYHATGLLSFIRRVKLSNPPSAGPIVVHCSAGAGRTGCYIVIDIMLD  1100
1101  MAEREGVVDIYNCVKALRSRRINMVQTEEQYIFIHDAILEACLCGETAIP  1150
1151  VCEFKAAYFDMIRIDSQTNSSHLKDEFQTLNSVTPRLQAEDCSIACLPRN  1200
1201  HDKNRFMDMLPPDRCLPFLITIDGESSNYINAALMDSYRQPAAFIVTQYP  1250
1251  LPNTVKDFWRLVYDYGCTSIVMLNEVDLSQGCPQYWPEEGMLRYGPIQVE  1300
1301  CMSCSMDCDVINRIFRICNLTRPQEGYLMVQQFQYLGWASHREVPGSKRS  1350
1351  FLKLILQVEKWQEECEEGEGRTIIHCLNGGGRSGMFCAIGIVVEMVKRQN  1400
1401  VVDVFHAVKTLRNSKPNMVEAPEQYRFCYDVALEYLESS             1439

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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