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

NucPred

Fetching P02463 from www.uniprot.org...

The NucPred score for your sequence is 0.25 (see score help below)

   1  MGPRLSVWLLLLFAALLLHEERSRAAAKGDCGGSGCGKCDCHGVKGQKGE    50
  51  RGLPGLQGVIGFPGMQGPEGPHGPPGQKGDAGEPGLPGTKGTRGPPGAAG   100
 101  YPGNPGLPGIPGQDGPPGPPGIPGCNGTKGERGPLGPPGLPGFSGNPGPP   150
 151  GLPGMKGDPGEILGHVPGTLLKGERGFPGIPGMPGSPGLPGLQGPVGPPG   200
 201  FTGPPGPPGPPGPPGEKGQMGSSFQGPKGDKGEQGVSGPPGVPGQAQVKE   250
 251  KGDFAPTGEKGQKGEPGFPGVPGYGEKGEPGKQGPRGKPGKDGEKGERGS   300
 301  PGIPGDSGYPGLPGRQGPQGEKGEAGLPGPPGTVIGTMPLGEKGDRGYPG   350
 351  APGLRGEPGPKGFPGTPGQPGPPGFPTPGQAGAPGFPGERGEKGDQGFPG   400
 401  VSLPGPSGRDGAPGPPGPPGPPGQPGHTNGIVECQPGPPGDQGPPGTPGQ   450
 451  PGLTGEVGQKGQKGESCLACDTEGLRGPPGPQGPPGEIGFPGQPGAKGDR   500
 501  GLPGRDGLEGLPGPQGSPGLIGQPGAKGEPGEIFFDMRLKGDKGDPGFPG   550
 551  QPGMPGRAGTPGRDGHPGLPGPKGSPGSIGLKGERGPPGGVGFPGSRGDI   600
 601  GPPGPPGVGPIGPVGEKGQAGFPGGPGSPGLPGPKGEAGKVVPLPGPPGA   650
 651  AGLPGSPGFPGPQGDRGFPGTPGRPGIPGEKGAVGQPGIGFPGLPGPKGV   700
 701  DGLPGEIGRPGSPGRPGFNGLPGNPGPQGQKGEPGIGLPGLKGQPGLPGI   750
 751  PGTPGEKGSIGGPGVPGEQGLTGPPGLQGIRGDPGPPGVQGPAGPPGVPG   800
 801  IGPPGAMGPPGGQGPPGSSGPPGIKGEKGFPGFPGLDMPGPKGDKGSQGL   850
 851  PGLTGQSGLPGLPGQQGTPGVPGFPGSKGEMGVMGTPGQPGSPGPAGTPG   900
 901  LPGEKGDHGLPGSSGPRGDPGFKGDKGDVGLPGMPGSMEHVDMGSMKGQK   950
 951  GDQGEKGQIGPTGDKGSRGDPGTPGVPGKDGQAGHPGQPGPKGDPGLSGT  1000
1001  PGSPGLPGPKGSVGGMGLPGSPGEKGVPGIPGSQGVPGSPGEKGAKGEKG  1050
1051  QSGLPGIGIPGRPGDKGDQGLAGFPGSPGEKGEKGSAGTPGMPGSPGPRG  1100
1101  SPGNIGHPGSPGLPGEKGDKGLPGLDGVPGVKGEAGLPGTPGPTGPAGQK  1150
1151  GEPGSDGIPGSAGEKGEQGVPGRGFPGFPGSKGDKGSKGEVGFPGLAGSP  1200
1201  GIPGVKGEQGFMGPPGPQGQPGLPGTPGHPVEGPKGDRGPQGQPGLPGHP  1250
1251  GPMGPPGFPGINGPKGDKGNQGWPGAPGVPGPKGDPGFQGMPGIGGSPGI  1300
1301  TGSKGDMGLPGVPGFQGQKGLPGLQGVKGDQGDQGVPGPKGLQGPPGPPG  1350
1351  PYDVIKGEPGLPGPEGPPGLKGLQGPPGPKGQQGVTGSVGLPGPPGVPGF  1400
1401  DGAPGQKGETGPFGPPGPRGFPGPPGPDGLPGSMGPPGTPSVDHGFLVTR  1450
1451  HSQTTDDPLCPPGTKILYHGYSLLYVQGNERAHGQDLGTAGSCLRKFSTM  1500
1501  PFLFCNINNVCNFASRNDYSYWLSTPEPMPMSMAPISGDNIRPFISRCAV  1550
1551  CEAPAMVMAVHSQTIQIPQCPNGWSSLWIGYSFVMHTSAGAEGSGQALAS  1600
1601  PGSCLEEFRSAPFIECHGRGTCNYYANAYSFWLATIERSEMFKKPTPSTL  1650
1651  KAGELRTHVSRCQVCMRRT                                 1669

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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