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

NucPred

Fetching P54276 from www.uniprot.org...

The NucPred score for your sequence is 0.82 (see score help below)

   1  MSRQSTLYSFFPKSPALGDTKKAAAEASRQGAAASGASASRGGDAAWSEA    50
  51  EPGSRSAAVSASSPEAKDLNGGLRRASSSAQAVPPSSCDFSPGDLVWAKM   100
 101  EGYPWWPCLVYNHPFDGTFIRKKGKSVRVHVQFFDDSPTRGWVSKRMLKP   150
 151  YTGSKSKEAQKGGHFYSSKSEILRAMQRADEALSKDTAERLQLAVCDEPS   200
 201  EPEEEEETEVHEAYLSDKSEEDNYNESEEEAQPSVQGPRRSSRQVKKRRV   250
 251  ISDSESDIGGSDVEFKPDTKQEGSSDDASSGVGDSDSEDLGTFGKGAPKR   300
 301  KRAMVAQGGLRRKSLKKETGSAKRATPILSETKSTLSAFSAPQNSESQTH   350
 351  VSGGGNDSSGPTVWYHETLEWLKPEKRRDEHRRRPDHPEFNPTTLYVPEE   400
 401  FLNSCTPGMRKWWQLKSQNFDLVIFYKVGKFYELYHMDAVIGVSELGLIF   450
 451  MKGNWAHSGFPEIAFGRFSDSLVQKGYKVARVEQTETPEMMEARCRKMAH   500
 501  VSKFDRVVRREICRIITKGTQTYSVLDGDPSENYSRYLLSLKEKEEETSG   550
 551  HTRVYGVCFVDTSLGKFFIGQFSDDRHCSRFRTLVAHYPPVQILFEKGNL   600
 601  STETKTVLKGSLSSCLQEGLIPGSQFWDATKTLRTLLEGGYFTGNGDSST   650
 651  VLPLVLKGMTSESDSVGLTPGEESELALSALGGIVFYLKKCLIDQELLSM   700
 701  ANFEEYFPLDSDTVSTVKPGAVFTKASQRMVLDAVTLNNLEIFLNGTNGS   750
 751  TEGTLLERLDTCHTPFGKRLLKQWLCAPLCSPSAISDRLDAVEDLMAVPD   800
 801  KVTEVADLLKKLPDLERLLSKIHNVGSPLKSQNHPDSRAIMYEETTYSKK   850
 851  KIIDFLSALEGFKVMCKVSGLLEEVAGGFTSKTLKQVVTLQSKSPKGRFP   900
 901  DLTAELQRWDTAFDHEKARKTGLITPKAGFDSDYDQALADIRENEQSLLE   950
 951  YLDKQRSRLGCKSIVYWGIGRNRYQLEIPENFATRNLPEEYELKSTKKGC  1000
1001  KRYWTKTIEKKLANLINAEERRDTSLKDCMRRLFCNFDKNHKDWQSAVEC  1050
1051  IAVLDVLLCLANYSQGGDGPMCRPEIVLPGEDTHPFLEFKGSRHPCITKT  1100
1101  FFGDDFIPNDILIGCEEEAEEHGKAYCVLVTGPNMGGKSTLIRQAGLLAV  1150
1151  MAQLGCYVPAEKCRLTPVDRVFTRLGASDRIMSGESTFFVELSETASILR  1200
1201  HATAHSLVLVDELGRGTATFDGTAIANAVVKELAETIKCRTLFSTHYHSL  1250
1251  VEDYSKSVCVRLGHMACMVENECEDPSQETITFLYKFIKGACPKSYGFNA  1300
1301  ARLANLPEEVIQKGHRKAREFERMNQSLQLFREVCLATEKPTINGEAIHR  1350
1351  LLALINGL                                            1358

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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