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

NucPred

Fetching P52701 from www.uniprot.org...

The NucPred score for your sequence is 0.88 (see score help below)

   1  MSRQSTLYSFFPKSPALSDANKASARASREGGRAAAAPGASPSPGGDAAW    50
  51  SEAGPGPRPLARSASPPKAKNLNGGLRRSVAPAAPTSCDFSPGDLVWAKM   100
 101  EGYPWWPCLVYNHPFDGTFIREKGKSVRVHVQFFDDSPTRGWVSKRLLKP   150
 151  YTGSKSKEAQKGGHFYSAKPEILRAMQRADEALNKDKIKRLELAVCDEPS   200
 201  EPEEEEEMEVGTTYVTDKSEEDNEIESEEEVQPKTQGSRRSSRQIKKRRV   250
 251  ISDSESDIGGSDVEFKPDTKEEGSSDEISSGVGDSESEGLNSPVKVARKR   300
 301  KRMVTGNGSLKRKSSRKETPSATKQATSISSETKNTLRAFSAPQNSESQA   350
 351  HVSGGGDDSSRPTVWYHETLEWLKEEKRRDEHRRRPDHPDFDASTLYVPE   400
 401  DFLNSCTPGMRKWWQIKSQNFDLVICYKVGKFYELYHMDALIGVSELGLV   450
 451  FMKGNWAHSGFPEIAFGRYSDSLVQKGYKVARVEQTETPEMMEARCRKMA   500
 501  HISKYDRVVRREICRIITKGTQTYSVLEGDPSENYSKYLLSLKEKEEDSS   550
 551  GHTRAYGVCFVDTSLGKFFIGQFSDDRHCSRFRTLVAHYPPVQVLFEKGN   600
 601  LSKETKTILKSSLSCSLQEGLIPGSQFWDASKTLRTLLEEEYFREKLSDG   650
 651  IGVMLPQVLKGMTSESDSIGLTPGEKSELALSALGGCVFYLKKCLIDQEL   700
 701  LSMANFEEYIPLDSDTVSTTRSGAIFTKAYQRMVLDAVTLNNLEIFLNGT   750
 751  NGSTEGTLLERVDTCHTPFGKRLLKQWLCAPLCNHYAINDRLDAIEDLMV   800
 801  VPDKISEVVELLKKLPDLERLLSKIHNVGSPLKSQNHPDSRAIMYEETTY   850
 851  SKKKIIDFLSALEGFKVMCKIIGIMEEVADGFKSKILKQVISLQTKNPEG   900
 901  RFPDLTVELNRWDTAFDHEKARKTGLITPKAGFDSDYDQALADIRENEQS   950
 951  LLEYLEKQRNRIGCRTIVYWGIGRNRYQLEIPENFTTRNLPEEYELKSTK  1000
1001  KGCKRYWTKTIEKKLANLINAEERRDVSLKDCMRRLFYNFDKNYKDWQSA  1050
1051  VECIAVLDVLLCLANYSRGGDGPMCRPVILLPEDTPPFLELKGSRHPCIT  1100
1101  KTFFGDDFIPNDILIGCEEEEQENGKAYCVLVTGPNMGGKSTLMRQAGLL  1150
1151  AVMAQMGCYVPAEVCRLTPIDRVFTRLGASDRIMSGESTFFVELSETASI  1200
1201  LMHATAHSLVLVDELGRGTATFDGTAIANAVVKELAETIKCRTLFSTHYH  1250
1251  SLVEDYSQNVAVRLGHMACMVENECEDPSQETITFLYKFIKGACPKSYGF  1300
1301  NAARLANLPEEVIQKGHRKAREFEKMNQSLRLFREVCLASERSTVDAEAV  1350
1351  HKLLTLIKEL                                          1360

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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