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

NucPred

Fetching P28167 from www.uniprot.org...

The NucPred score for your sequence is 0.95 (see score help below)

   1  MSSFDVETFNGKIVYNLDGSAHIIATDNTNGGGSGSGQNCYGSTTNSLKN    50
  51  LSKDKGRGQEEKDIEHPSQYHREQSDNKRQEEAVDNRPGVESLGSACYKS   100
 101  SPKIHSFRVVSAQDANSTCQDQIRAFKIQKPILMCFICKLSFGNVKSFSL   150
 151  HANTEHRLNLEELDQQLLNREYSSAIIQRNMDEKPQISFLQPLANNDASA   200
 201  DTNDTEKLQTATEGSDATLPSSPQPVFRNVSELEPENKQETEQNRLLNQD   250
 251  REQEPESDQHTSSSKMAAPSAYIPLSSPKVAGKLTVKFGSLNSATAKTNN   300
 301  LSKVSSTSSPPSTYASGEVLSPSTDNISNHKSTHCNQETEPPSSSSSEVE   350
 351  MKIGSMSTSPQTNDSDVPCSGFLQMQHMTTGGAYTPQVSSFHASLAALAA   400
 401  NESNDNRVKLITEFLQQQLQQHQSSLFPSPCPDHPDLNGVDCKTCELLDI   450
 451  QQRSKSPSSSHHQFSQSLPQLQIQSQPQQTPHRSPCSNSVALPVSPSASS   500
 501  VASVGNASTATSSFTIGACSEHINGRPQGVDCARCEMLLNSARLNSGVQM   550
 551  STRNSCKTLKCPQCNWHYKYQETLEIHMREKHPDGESACGYCLAGQQHPR   600
 601  LARGESYSCGYKPYRCEICNYSTTTKGNLSIHMQSDKHLNNMQELNSSQN   650
 651  MVAAAAAAAVTGKLLLSSSSPQVTAACPSNSGSGAGSGSSNIVGGTASLS   700
 701  GNATPSVTGANSSNANAGSNTNNAGTKPKPSFRCDICSYDTSVARNLRIH   750
 751  MTSEKHTHNMAVLQNNIKHIQAFNFLQQQQQSGTGNIASHSSGSFMPEVA   800
 801  LADLAYNQALMIQLLHQQQQHQQSANTKLSPSSSPVSTPDQFSFSPKPIK   850
 851  LNHGTGAAMGIGMAMGMGMSHSNEVSCELSGDPHPLTKTDKWPMAFYSCL   900
 901  VCDCYSTNNLDDLNQHLLLDRSRQSSSASSEIMVIHNNNYICRLCNYKTN   950
 951  LKANFQLHSKTDKHLQKLNFINHIREGGPQNEYKMQYQQQQLAANVVQLK  1000
1001  CNCCDFHTNSIQKLSLHTQQMRHDTMRMIFQHLLYIVQQSEMHNKSSGSA  1050
1051  EDDPQCACPDEDQQLQLQSSKKLLLCQLCNFTAQNIHEMVQHVKGIRHLQ  1100
1101  VEQFICLQRRSENQEIPALNEVFKVTEWVMENEDVSLAPGLNLARTTTND  1150
1151  ATTDASYAAASSAAVPAIPDVSMFSPTSPSSCATSCDKNLSQIVLPNVNN  1200
1201  LGSGVPTTVFKCNLCEYFVQSKSEIAAHIETEHSCAESDEFITIPTNTAA  1250
1251  LQAFQTAVAAAALAAVHQRCAVINPPTQDTVDEDKDLDTNVSDGPVGIKQ  1300
1301  ERLEQEVDRTTSMDVTKDLASQATDFGAPESPKVAETEVGVQCPLCLENH  1350
1351  FREKQYLEDHLTSVHSVTRDGLSRLLLLVDQKALKKESTDIACPTDKAPY  1400
1401  ANTNALERAPTPIENTCNVSLIKSTSANPSQSVSLQGLSCQQCEASFKHE  1450
1451  EQLLKHAQQNQHFSLQNGEYLCLAASHISRPCFMTFRTIPTMISHFQDLH  1500
1501  MSLIISERHVYKYRCKQCSLAFKTQEKLTTHMLYHSMRDATKCSFCQRNF  1550
1551  RSTQALQKHMEQAHAEDGTPSTRTNSPQTPMLSTEETHKHLLAESHAVER  1600
1601  EVSGSDVSPIELETHLNKETRHLSPTPMSLDSQSHQKHLATFAALLKQQQ  1650
1651  CNSDAGGLHPEALSMSTGEMPPQLQGLQNLQHIQQHFGAVAAAAGLPINP  1700
1701  VDMLNIMQFHHLMSLNFMNLAPPLVFGANAAGNAVSGPSALNNSITTSTA  1750
1751  TSASGLGDTHLTSGVSSIPVDSGKATAVPPQTQLNANANSQQLASNQKRA  1800
1801  RTRITDDQLKILRAHFDINNSPSEESIMEMSQKANLPMKVVKHWFRNTLF  1850
1851  KERQRNKDSPYNFNNPPSTTLNLEEYERTGQAKVTPLNDTCSVAVTGPMT  1900
1901  SSTISLPPSGNINLSSKENATSKVLAAGKANASGPVTFSATVPVSTPLSR  1950
1951  PESTNSSGNISDYIGNNIFFGQLGSKEQILPYSLDGQIKSEPQDDMIGAT  2000
2001  DFAYQTKQHSSFSFLKQQQDLVDPPEQCLTNQNADTAQDQSLLAGSSLAS  2050
2051  NCQSQQQINIFETKSESGSSDVLSRPPSPNSGAAGNVYGSMNDLLNQQLE  2100
2101  NMGSNMGPPKKMQIVGKTFEKNVAPMVTSGSVSTQFESNSSNSSSSSSST  2150
2151  SGGKRANRTRFTDYQIKVLQEFFENNSYPKDSDLEYLSKLLLLSPRVIVV  2200
2201  WFQNARQKQRKIYENQPNNTLFENEETKKQNINYACKKCNLVFQRYYELI  2250
2251  RHQKNHCFKEENNKKSAKAQIAAAQIAQNLSSEDSNSSMDIHHVGICPPG  2300
2301  SAVASHTLSTPGSAAPLPGQYTQHSFGALPSPQHLFAKSSSLTDFSPSTT  2350
2351  PTPPQRERSNSLDQIQRPPKFDCDKCELNFNQLEKLREHQLLHLMNPGNI  2400
2401  CSDVGQNSNPEANFGPFGSILQSLQQAAAQQQQQHHQQPPTKKRKYSDCS  2450
2451  SNADEMQSLSELEASQKKHEYLYKYFMQNETSQEVKQQFLMQQQQKKLEQ  2500
2501  GNECDFELDFLTNFYQQNELKKVSNYDFLLQYYRTHEEAKSSQQHTFSSS  2550
2551  KKPTIEFLLQYYQLNESKKFFQLVASPQIIPDVPGYKPSLRIPKSTSDEA  2600
2601  PYIGETSLEQATELQREKQDEQLRIDRPSEENDLSMNKNKVENINNNNIN  2650
2651  VDQSNLTETNGGVPSVETKEECTQESSLIAMDDENKYLCTRSKQKDDKEK  2700
2701  SHYLHNLEDFLDATMIENNSQTLTFNDDEKACQKDELTQNSNAIEKRSSV  2750
2751  SPVNVSSKQNKRLRTTILPEQLNFLYECYQSESNPSRKMLEEISKKVNLK  2800
2801  KRVVQVWFQNSRAKDKKSRNQRHYAHISDDNSYDGSSGKEVYSDLRSNGI  2850
2851  TVDTDLETNLQDCQLCQVTQVNIRKHAFSVEHISKMKKLLEQTTELYAQS  2900
2901  NGSGSEDNDSDREKRFYNLSKAFLLQHVVTNATSHAIHTARQDSDVIAEG  2950
2951  NCILNYDTNGGDSKSHVQHNLPNEVVSEDARKIAGNQELMQQLFNRNHIT  3000
3001  VIGGK                                               3005

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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