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

NucPred

Fetching P07702 from www.uniprot.org...

The NucPred score for your sequence is 0.14 (see score help below)

   1  MTNEKVWIEKLDNPTLSVLPHDFLRPQQEPYTKQATYSLQLPQLDVPHDS    50
  51  FSNKYAVALSVWAALIYRVTGDDDIVLYIANNKILRFNIQPTWSFNELYS   100
 101  TINNELNKLNSIEANFSFDELAEKIQSCQDLERTPQLFRLAFLENQDFKL   150
 151  DEFKHHLVDFALNLDTSNNAHVLNLIYNSLLYSNERVTIVADQFTQYLTA   200
 201  ALSDPSNCITKISLITASSKDSLPDPTKNLGWCDFVGCIHDIFQDNAEAF   250
 251  PERTCVVETPTLNSDKSRSFTYRDINRTSNIVAHYLIKTGIKRGDVVMIY   300
 301  SSRGVDLMVCVMGVLKAGATFSVIDPAYPPARQTIYLGVAKPRGLIVIRA   350
 351  AGQLDQLVEDYINDELEIVSRINSIAIQENGTIEGGKLDNGEDVLAPYDH   400
 401  YKDTRTGVVVGPDSNPTLSFTSGSEGIPKGVLGRHFSLAYYFNWMSKRFN   450
 451  LTENDKFTMLSGIAHDPIQRDMFTPLFLGAQLYVPTQDDIGTPGRLAEWM   500
 501  SKYGCTVTHLTPAMGQLLTAQATTPFPKLHHAFFVGDILTKRDCLRLQTL   550
 551  AENCRIVNMYGTTETQRAVSYFEVKSKNDDPNFLKKLKDVMPAGKGMLNV   600
 601  QLLVVNRNDRTQICGIGEIGEIYVRAGGLAEGYRGLPELNKEKFVNNWFV   650
 651  EKDHWNYLDKDNGEPWRQFWLGPRDRLYRTGDLGRYLPNGDCECCGRADD   700
 701  QVKIRGFRIELGEIDTHISQHPLVRENITLVRKNADNEPTLITFMVPRFD   750
 751  KPDDLSKFQSDVPKEVETDPIVKGLIGYHLLSKDIRTFLKKRLASYAMPS   800
 801  LIVVMDKLPLNPNGKVDKPKLQFPTPKQLNLVAENTVSETDDSQFTNVER   850
 851  EVRDLWLSILPTKPASVSPDDSFFDLGGHSILATKMIFTLKKKLQVDLPL   900
 901  GTIFKYPTIKAFAAEIDRIKSSGGSSQGEVVENVTANYAEDAKKLVETLP   950
 951  SSYPSREYFVEPNSAEGKTTINVFVTGVTGFLGSYILADLLGRSPKNYSF  1000
1001  KVFAHVRAKDEEAAFARLQKAGITYGTWNEKFASNIKVVLGDLSKSQFGL  1050
1051  SDEKWMDLANTVDIIIHNGALVHWVYPYAKLRDPNVISTINVMSLAAVGK  1100
1101  PKFFDFVSSTSTLDTEYYFNLSDKLVSEGKPGILESDDLMNSASGLTGGY  1150
1151  GQSKWAAEYIIRRAGERGLRGCIVRPGYVTGASANGSSNTDDFLLRFLKG  1200
1201  SVQLGKIPDIENSVNMVPVDHVARVVVATSLNPPKENELAVAQVTGHPRI  1250
1251  LFKDYLYTLHDYGYDVEIESYSKWKKSLEASVIDRNEENALYPLLHMVLD  1300
1301  NLPESTKAPELDDRNAVASLKKDTAWTGVDWSNGIGVTPEEVGIYIAFLN  1350
1351  KVGFLPPPTHNDKLPLPSIELTQAQISLVASGAGARGSSAAA          1392

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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