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

NucPred

Fetching Q9ULM3 from www.uniprot.org...

The NucPred score for your sequence is 0.70 (see score help below)

   1  MSGIKRTIKETDPDYEDVSVALPNKRHKAIENSARDAAVQKIETIIKEQF    50
  51  ALEMKNKEHEIEVIDQRLIEARRMMDKLRACIVANYYASAGLLKVSEGSK   100
 101  TCDTMVFNHPAIKKFLESPSRSSSPANQRAETPSANHSESDSLSQHNDFL   150
 151  SDKDNNSNMDIEERLSNNMEQRPSRNTGRDTSRITGSHKTEQRNADLTDE   200
 201  TSRLFVKKTIVVGNVSKYIPPDKREENDQSTHKWMVYVRGSRREPSINHF   250
 251  VKKVWFFLHPSYKPNDLVEVREPPFHLTRRGWGEFPVRVQVHFKDSQNKR   300
 301  IDIIHNLKLDRTYTGLQTLGAETVVDVELHRHSLGEDCIYPQSSESDISD   350
 351  APPSLPLTIPAPVKASSPIKQSHEPVPDTSVEKGFPASTEAERHTPFYAL   400
 401  PSSLERTPTKMTTSQKVTFCSHGNSAFQPIASSCKIVPQSQVPNPESPGK   450
 451  SFQPITMSCKIVSGSPISTPSPSPLPRTPTSTPVHVKQGTAGSVINNPYV   500
 501  IMDKQPGQVIGATTPSTGSPTNKISTASQVSQGTGSPVPKIHGSSFVTST   550
 551  VKQEDSLFASMPPLCPIGSHPKVQSPKPITGGLGAFTKVIIKQEPGEAPH   600
 601  VPATGAASQSPLPQYVTVKGGHMIAVSPQKQVITPGEGIAQSAKVQPSKV   650
 651  VGVPVGSALPSTVKQAVAISGGQILVAKASSSVSKAVGPKQVVTQGVAKA   700
 701  IVSGGGGTIVAQPVQTLTKAQVTAAGPQKSGSQGSVMATLQLPATNLANL   750
 751  ANLPPGTKLYLTTNSKNPSGKGKLLLIPQGAILRATNNANLQSGSAASGG   800
 801  SGAGGGGGGGGGGGSGSGGGGSTGGGGGTAGGGTQSTAGPGGISQHLTYT   850
 851  SYILKQTPQGTFLVGQPSPQTSGKQLTTGSVVQGTLGVSTSSAQGQQTLK   900
 901  VISGQKTTLFTQAAHGGQASLMKISDSTLKTVPATSQLSKPGTTMLRVAG   950
 951  GVITTATSPAVALSANGPAQQSEGMAPVSSSTVSSVTKTSGQQQVCVSQA  1000
1001  TVGTCKAATPTVVSATSLVPTPNPISGKATVSGLLKIHSSQSSPQQAVLT  1050
1051  IPSQLKPLSVNTSGGVQTILMPVNKVVQSFSTSKPPAILPVAAPTPVVPS  1100
1101  SAPAAVAKVKTEPETPGPSCLSQEGQTAVKTEESSELGNYVIKIDHLETI  1150
1151  QQLLTAVVKKIPLITAKSEDASCFSAKSVEQYYGWNIGKRRAAEWQRAMT  1200
1201  MRKVLQEILEKNPRFHHLTPLKTKHIAHWCRCHGYTPPDPESLRNDGDSI  1250
1251  EDVLTQIDSEPECPSSFSSADNLCRKLEDLQQFQKREPENEEEVDILSLS  1300
1301  EPVKINIKKEQEEKQEEVKFYLPPTPGSEFIGDVTQKIGITLQPVALHRN  1350
1351  VYASVVEDMILKATEQLVNDILRQALAVGYQTASHNRIPKEITVSNIHQA  1400
1401  ICNIPFLDFLTNKHMGILNEDQ                              1422

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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