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

NucPred

Fetching Q9EPN1 from www.uniprot.org...

The NucPred score for your sequence is 0.56 (see score help below)

   1  MASDKPGPGLEPQPVALLAVGAGGGAGGGGAMGEPRGAAGSGPVVLPAGM    50
  51  INPSVPIRNIRMKFAVLIGLIQVGEVSNRDIVETVLNLLVGGEFDLEMNF   100
 101  IIQDAESITCMTELLEHCDVTCQAEIWSMFTAILRKSVRNLQTSTEVGLI   150
 151  EQVLLKMSAVDDMIADLLVDMLGVLASYSITVKELKLLFSMLRGESGIWP   200
 201  RHAVKLLSVLNQMPQRHGPDTFFNFPGCSAAAIALPPIAKWPYQNGFTLN   250
 251  TWFRMDPLNNINVDKDKPYLYCFRTSKGVGYSAHFVGNCLIVTSLKSKGK   300
 301  GFQHCVKYDFQPRKWYMISIVHIYNRWRNSEIRCYVNGQLVSYGDMAWHV   350
 351  NTNDSYDKCFLGSSETADANRVFCGQLGAVYVFSEALNPAQIFAVHQLGP   400
 401  GYKSTFKFKSESDIHLAEHHKQVLYDGKLASSIAFSYNAKATDAQLCLES   450
 451  SPKENASIFVHSPHALMLQDVKAIVTHSIHSAIHSIGGIQVLFPLFAQLD   500
 501  NRQLNDSQVETTVCATLLAFLVELLKSSVAMQEQMLGGKGFLVIGYLLEK   550
 551  SSRVHITRAVLEQFLSFAKYLDGLSHGAPLLKQLCDHILFNPAIWIHTPA   600
 601  KVQLSLYTYLSAEFIGTATIYTTIRRVGTVLQLMHTLKYYYWVINPADSS   650
 651  GIAPKGLDGPRPSQKEIISLRAFMLLFLKQLILKDRGVKEDELQSILNYL   700
 701  LTMHEDENIHDVLQLLVALMSEHPASMIPAFDQRNGIRVIYKLLASKSES   750
 751  IWVQALKVLGYFLKHLGHKRKVEIMHTHSLFTLLGERLMLHTNTVTVTTY   800
 801  NTLYEILTEQVCTQVVHKPHPEPDSTVKIQNPMILKVVATLLKNSTPSAE   850
 851  LMEVRRLFLSDMIKLFSNSRENRRCLLQCSVWQDWMFSLGYINPKSSEEQ   900
 901  KITEMVYNIFRILLYHAIKYEWGGWRVWVDTLSIAHSKVTYEAHKEYLAK   950
 951  MYEEYQRQEEENIKKGKKGNVSTISGLSSQTAGAKGGMEIREIEDLSQSQ  1000
1001  SPESETDYPVSTDTRDLLMSTKVSDDILGSSDRPGSGVHVEVHDLLVDIK  1050
1051  AEKVEATEVKLDDMDLSPETLVGGENGALVEVESLLDNVYSAAVEKLQNN  1100
1101  VHGSVGIIKKNEEKDNGPLITLADEKEELPNSSTPFLFDKIPRQEEKLLP  1150
1151  ELSSNHIIPNIQDTQVHLGVSDDLGLLAHMTASVELTCTSSIMEEKDFRI  1200
1201  HTTSDGVSSVSERELASSTKGLDYAEMTATTLETESSNSKAVPNVDAGSI  1250
1251  ISDTERSDDGKESGKEIRKIQTTATTQAVQGRSSTQQDRDLRVDLGFRGM  1300
1301  PMTEEQRRQFSPGPRTTMFRIPEFKWSPMHQRLLTDLLFALETDVHVWRS  1350
1351  HSTKSVMDFVNSNENIIFVHNTIHLISQMVDNIIIACGGILPLLSAATSP  1400
1401  TGSKTELENIEVTQGMSAETAVTFLSRLMAMVDVLVFASSLNFSEIEAEK  1450
1451  NMSSGGLMRQCLRLVCCVAVRNCLECRQRQRDRGSKSSHGSSKPQEAPHS  1500
1501  VTAASASKTPLENVPGNLSPIKDPDRLLQDVDINRLRAVVFRDVDDSKQA  1550
1551  QFLALAVVYFISVLMVSKYRDILEPQRETARTGSQPGRNIRQEINSPTST  1600
1601  VVVIPSIPHPSLNHGLLAKLMPEQSFAHSFYKETPATFPDTVKEKETPTP  1650
1651  GEDIQLESSVPHTDSGMGEEQVASILDGAELEPAAGPDAMSELLSTLSSE  1700
1701  VKKSQESLTEHPSEMLKPAPSISSISQTKGINVKEILKSLVAAPVEIAEC  1750
1751  GPEPIPYPDPALKREAHAILPMQFHSFDRSVVVPVKKPPPGSLAVTTVGA  1800
1801  TAAGSGLPTGSTSSIFAAPGATPKSMINTTGAVDSGSSSSSSSSSFVNGA  1850
1851  TSKNLPAVQTVAPMPEDSAENMSITAKLERALEKVAPLLREIFVDFAPFL  1900
1901  SRTLLGSHGQELLIEGLVCMKSSTSVVELVMLLCSQEWQNSIQKNAGLAF  1950
1951  IELINEGRLLCHAMKDHIVRVANEAEFILNRQRAEDVHKHAEFESQCAQY  2000
2001  AADRREEEKMCDHLISAAKHRDHVTANQLKQKILNILTNKHGAWGAVSHS  2050
2051  QLHDFWRLDYWEDDLRRRRRFVRNAFGSTHAEALLKSAVEYGTEEDVVKS  2100
2101  KKAFRSQAIVNQNSETELMLEGDDDAVSLLQEKEIDNLAGPVVLSTPAQL  2150
2151  IAPVVVAKGTLSITTTEIYFEVDEDDAAFKKIDTKVLAYTEGLHGKWMFS  2200
2201  EIRAVFSRRYLLQNTALEVFMANRTSVMFNFPDQATVKKVVYSLPRVGVG  2250
2251  TSYGLPQARRISLATPRQLYKSSNMTQRWQRREISNFEYLMFLNTIAGRT  2300
2301  YNDLNQYPVFPWVLTNYESEELDLTLPGNFRDLSKPIGALNPKRAVFYAE  2350
2351  RYETWEEDQSPPFHYNTHYSTATSALSWLVRIEPFTTFFLNANDGKFDHP  2400
2401  DRTFSSIARSWRTSQRDTSDVKELIPEFYYLPEMFVNSNGYHLGVREDEV  2450
2451  VVNDVDLPPWAKKPEDFVRINRMALESEFVSCQLHQWIDLIFGYKQRGPE  2500
2501  AVRALNVFHYLTYEGSVNLDSITDPVLREAMEAQIQNFGQTPSQLLIEPH  2550
2551  PPRSSAMHLCFLPQSPLMFKDQMQQDVIMVLKFPSNSPVTHVAANTLPHL  2600
2601  TIPAVVTVTCSRLFAVNRWHNTVGLRGAPGYSLDQAHHLPIEMDPLIANN  2650
2651  SGVNKRQITDLVDQSIQINAHCFVVTADNRYILICGFWDKSFRVYSTETG  2700
2701  KLTQIVFGHWDVVTCLARSESYIGGDCYIVSGSRDATLLLWYWSGRHHII  2750
2751  GDNPNSSDYPAPRAVLTGHDHEVVCVSVCAELGLVISGAKEGPCLVHTIT  2800
2801  GDLLRALEGPENCLFPRLISVSSEGHCIIYYERGRFSNFSINGKLLAQME  2850
2851  INDSTRAILLSSDGQNLVTGGDNGVVEVWQACDFKQLYIYPGCDAGIRAM  2900
2901  DLSHDQRTLITGMASGSIVAFNIDFNRWHYEHQNRY                2936

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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