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

NucPred

Fetching P49466 from www.uniprot.org...

The NucPred score for your sequence is 0.93 (see score help below)

   1  MNYSTALPDFIEMQRVSFCWFIAQGLNDELTMFSRIHDFSYNTEYRLFGQ    50
  51  EYSLVKPVYTIVRAKKLAANYSVQLVIPLEVRNKKLNSVRYFGQFTIINL   100
 101  PLMTTTATFVINGCERVIVSQIIRSPGIYFEKNKNHRKRKQFKSQVLGHA   150
 151  SKLGSFLPSGVPWIIPYDQPWKPWIIGKKLGYSKYNSNKDTKLDFYFYSL   200
 201  KSFKIYQKISKITNSPIKVQRIKLFLQWLKLNQKEFNFKNKLNLSELSFL   250
 251  LNYWNFIFKFIIKYQFLYKKNFEESYQIQESEFKTIFKTWNNQPQLNDSF   300
 301  SLKKIDQLTSNYEKKIQFYHNVIQQQFFDNLMLVPFITKEKKILNLESLA   350
 351  NRQKQKKISLTLLTNESIKFAFYFSPSLKEVFKYRSPKKRKPKEAKIKQP   400
 401  ILYLRSPSKIVQFKDDHQVLDSYKKKYDTKDFYTATLIPESGSWIRFGFQ   450
 451  KNTKINRYQYPIRHQEDEVIIQIDKITQKPILYLLKEMGLTDWEICSNLK   500
 501  HADFFYFTKPFLTGSLTSKQPLPRFDLHSDYYKNISEFSHIFDARYYRLG   550
 551  KIGRFQINNRLNLKLNNRIYTITYEDIFAILDCLVTLSISKTTGDDIDHL   600
 601  KNRRVRSVGELLQNLFRVGFQRLVRKLGSQINKRESGQISSFNIIGATVR   650
 651  EFFGSSQLSQYMDQTNPLSSLTHRRRISGLGPGGLDRDRISFAVRDIHPS   700
 701  HYGRICPIETPEGPNVGLIASLTTCARVNKLGFIETPFWRVINGKVIKTG   750
 751  NPIYLTADIEDFYKIAPADISTNSKNYLTQNLIPVRYKQDFITVSPFQVD   800
 801  FISISPIQVVSVATSLIPFFEHDDANRALMGSNMQRQSVPLMLSQKPIVG   850
 851  TGLENQIAIDSGMTINAQGAGIVHSVTADYIIVKEYSGRKLKYILQKYQR   900
 901  SNQETCINHRPIVWKGEKIKSGQILTDGPGITNNELALGQNVLVAYMPWQ   950
 951  GYNFEDAILINERLVYEDVFTSIHIERYDIEIEQDDDVSEQITKNIPNLS  1000
1001  FSEIQNLNDDGIVALGTFVKPGDILVGKIIAKNDSEQLPEAKLLRAIFGA  1050
1051  KAKGVRDTSFRMPKGKYGRVVDRVTFNRKTKLAYKFEKIQVFIAQIRKIK  1100
1101  VGDKIAGRHGNKGIISRILPRQDMPFLPDGTPVDIILNPLGVPSRMNVGQ  1150
1151  LYECLLGIAGHKLNRRFKILPFDEMYGPEVSRILINKKLRQASIENDEAW  1200
1201  LFNPYSPGKMVLIDGRTGKEFENPITVGNAYMLKLIHLVDDKMHARATGP  1250
1251  YSLITQQPLGGKAQHGGQRFGEMEVWALEGFGAAFTLKELLTIKSDDMQG  1300
1301  RNETLNAIVKGQLIPKSGVPESFKVLLQELRSIGLDMSTYKIENYNLNQH  1350
1351  YELEVDLIETYDSLEKTFPPTSNLDDISF                       1379

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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