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

NucPred

Fetching O15013 from www.uniprot.org...

The NucPred score for your sequence is 0.74 (see score help below)

   1  MRPPGFLSRAPSLNRAERGIWSCSMDQREPLPPAPAENEMKYDTNNNEEE    50
  51  EGEQFDFDSGDEIPEADRQAPSAPETGGAGASEAPAPTGGEDGAGAETTP   100
 101  VAEPTKLVLPMKVNPYSVIDITPFQEDQPPTPVPSAEEENVGLHVPCGYL   150
 151  VPVPCGYAVPSNLPLLLPAYSSPVIICATSLDEEAETPEVTEDRQPNSLS   200
 201  SEEPPTSEDQVGREDSALARWAADPANTAWMENPEEAIYDDVPRENSDSE   250
 251  PDEMIYDDVENGDEGGNSSLEYGWSSSEFESYEEQSDSECKNGIPRSFLR   300
 301  SNHKKQLSHDLTRLKEHYEKKMRDLMASTVGVVEIQQLRQKHELKMQKLV   350
 351  KAAKDGTKDGLERTRAAVKRGRSFIRTKSLIAQDHRSSLEEEQNLFIDVD   400
 401  CKHPEAILTPMPEGLSQQQVVRRYILGSVVDSEKNYVDALKRILEQYEKP   450
 451  LSEMEPKVLSERKLKTVFYRVKEILQCHSLFQIALASRVSEWDSVEMIGD   500
 501  VFVASFSKSMVLDAYSEYVNNFSTAVAVLKKTCATKPAFLEFLKQEQEAS   550
 551  PDRTTLYSLMMKPIQRFPQFILLLQDMLKNTSKGHPDRLPLQMALTELET   600
 601  LAEKLNERKRDADQRCEVKQIAKAINERYLNKLLSSGSRYLIRSDDMIET   650
 651  VYNDRGEIVKTKERRVFMLNDVLMCATVSSRPSHDSRVMSSQRYLLKWSV   700
 701  PLGHVDAIEYGSSAGTGEHSRHLAVHPPESLAVVANAKPNKVYMGPGQLY   750
 751  QDLQNLLHDLNVIGQITQLIGNLKGNYQNLNQSVAHDWTSGLQRLILKKE   800
 801  DEIRAADCCRIQLQLPGKQDKSGRPTFFTAVFNTFTPAIKESWVNSLQMA   850
 851  KLALEEENHMGWFCVEDDGNHIKKEKHPLLVGHMPVMVAKQQEFKIECAA   900
 901  YNPEPYLNNESQPDSFSTAHGFLWIGSCTHQMGQIAIVSFQNSTPKVIEC   950
 951  FNVESRILCMLYVPVEEKRREPGAPPDPETPAVRASDVPTICVGTEEGSI  1000
1001  SIYKSSQGSKKVRLQHFFTPEKSTVMSLACTSQSLYAGLVNGAVASYARA  1050
1051  PDGSWDSEPQKVIKLGVLPVRSLLMMEDTLWAASGGQVFIISVETHAVEG  1100
1101  QLEAHQEEGMVISHMAVSGVGIWIAFTSGSTLRLFHTETLKHLQDINIAT  1150
1151  PVHNMLPGHQRLSVTSLLVCHGLLMVGTSLGVLVALPVPRLQGIPKVTGR  1200
1201  GMVSYHAHNSPVKFIVLATALHEKDKDKSRDSLAPGPEPQDEDQKDALPS  1250
1251  GGAGSSLSQGDPDAAIWLGDSLGSMTQKSDLSSSSGSLSLSHGSSSLEHR  1300
1301  SEDSTIYDLLKDPVSLRSKARRAKKAKASSALVVCGGQGHRRVHRKARQP  1350
1351  HQEELAPTVMVWQIPLLNI                                 1369

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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