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

NucPred

Fetching Q4PGG5 from www.uniprot.org...

The NucPred score for your sequence is 0.93 (see score help below)

   1  MTTQSSAGPQSQPSRDFFASSPSATPGSPSKQASQASKQPDALVTPSSVT    50
  51  ANTSVCADNLLDHHQHEPQHDAASVVIGIEGSINDSAAIHRRRQQPRRAA   100
 101  VQNAKPLFLAEDEDDAINWPVADQEEGDDFFSTQLADQKVKRPARATVNS   150
 151  DEKDFQPSTLSSLSSPPSETVYKRSPPSDDVQASNGPAKRPKKSWSTPAS   200
 201  VATTKSAPRHCSPTPDPRLRHDAFDRRYIGTFVLSAWSLSKGSSYVKPGD   250
 251  AVRIFRPRKKHATAEPKIASKLTNGGMQKAKQTTLNFRGASAGPTNFFTS   300
 301  KQKSKEKEHFIVRFSNMRGFEVGRLPLEVAIWMSKLIDAGIAEFEGVVVD   350
 351  CPPSLTVGCDIILQVKAYIKFDAFFSTLLGSREFDDQNEALRPETAESDL   400
 401  EKTLRERKISLLRMFRVCDLKPRLSNAILKSHKASDDFSSEAMLDQYGGD   450
 451  IQGAVKHAGSDLAISTENGTQLPHAGEDASSTAIDLDVEGDTDAVAHLEI   500
 501  ADQLQSAKRQTDGDADTEENDGTELNLNQLDQVYRKAQANDAHLPEVEPP   550
 551  ESFVLTLRPYQKQALGWMKNMEMAPGQSSSSQEQSVTQQGNGDTGERNVS   600
 601  LHPLWEEYEFPLDYDNPQANERLILSATRLFYFNPYTGDLSLDFQRASKG   650
 651  SRGGILADEMGLGKTIMVASLLHANRTSDPGEESEGEINAVDAAEGDVST   700
 701  KRKGSAKQTSLASAFAASTSSVDQRKALLKASVSKGKASLVVAPMSLIGQ   750
 751  WRDELIRASAPGSLTPVLYYADTKGDLLAQLESGKVDVVITSYGTLVTEY   800
 801  RRFLDGGGASNRHLSVSAPLYCIDWLRVILDEAHNIKNRSTMNARACCDL   850
 851  VSRRRWALTGTPIINRLTDLFSLLKFLRVEPWGDFSFFNSFVCKPFQAKS   900
 901  TKALDVVQVILESVLLRREKKMKDKDGKPIVELPPKTIVVKELEFSELER   950
 951  RIYDNVYRRAYLQFASLKANGTVTRNLSVIFSVLMRLRQAVCHPSLVLKA  1000
1001  GSKVQSGGIRKDHVDRNGEVGVGEGDHVEVDADAVEFGLDSDHVGGNGTP  1050
1051  STQDLRELVAQFQLDEAGEAGDDSTESYTKATVERLIGEMHGDQATVPPV  1100
1101  VSDGENECPICLEESQISPCYLPRCMHSACKACLVDYLGQCKQKGDQGAC  1150
1151  PTCRKGPVQETDLIEAIRTRPATRAASGGASPTDRPGKACTLTSVPSVIY  1200
1201  VRNNLRTSTKLSALISHLNTLRATEAAFKGVIFSQFTSFLDLIEPVLTRY  1250
1251  RFHFLRLDGSTPQKVRDKLVLEFQSPSPTNHVVLFLISLKAGGVGLNLTA  1300
1301  ANKIWLLDFWWNSSIENQAIDRIHRFGQTSPVSVFRYIIKDSIEDRILLI  1350
1351  QKRKDMLIKHALNTDNHPHGTKPNSEMLANLDLLFGE               1387

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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