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

NucPred

Fetching P00967 from www.uniprot.org...

The NucPred score for your sequence is 0.26 (see score help below)

   1  MSHRVLVIGSGGREHAICWKLSQSPKVAQIYALPGSHGIQLVEKCRNLDA    50
  51  KTLDPKDFEAIAKWSKENQIALVVVGPEDPLALGLGDVLQSAGIPCFGPG   100
 101  KQGAQIEADKKWAKDFMLRHGIPTARYESFTDTEKAKAFIRSAPYPALVV   150
 151  KAAGLAAGKGVVVAANAKEACQAVDEILGDLKYGQAGATLVVEELLEGEE   200
 201  VSVLAFTDGKSVRAMLPAQDHKRLGNGDTGPNTGGMGAYCPCPLISQPAL   250
 251  ELVQKAVLERAVQGLIKERINYQGVLYAGLMLTRDGPRVLEFNCRFGDPE   300
 301  TQVILPLLESDLFDVMEACCSGKLDKIPLQWRNGVSAVGVILASAGYPET   350
 351  STKGCIISGLPAANTPTQLVFHSGLAVNAQKEALTNGGRVLIAIALDGSL   400
 401  KEAAAKATKLAGSISFSGSGAQYRTDIAQKAFKIASASTPGLSYKDSGVD   450
 451  IDAGDALVQRIKPLSRGTQRPGVIGGLGGFGGLFRLKELTYKEPVIAEAT   500
 501  QGVGAKIHLALTHEFYENVGYDLFALAANDVLEVGAEPVAFLDYIACGKL   550
 551  QVPLAAQLVKGMADGCRDARCALVGGETAEMPSLYAPGQHDMAGYCVGIV   600
 601  EHSRILPRFDLYQPGDLLIGLPSSGLHCAGFNEILTQLAASKVNLRERSP   650
 651  VDGGDDGLTLAHVLATPTQLYVQQLLPHLQKGDEIKSVAHVTHGLLNDIL   700
 701  RLLPDGFETTLDFGAVPVPKIFGWLAGKLKLSAQTILERHNCGIGMVLIL   750
 751  PQSSQLWRTSLPGAKVLGVLQRRSKVSGSPVQVRNFVEQLEKVASPFGGL   800
 801  GDRELPEELKKLPSNSDLSAPREECFENAAGRRLTRIPTHYKDPILILGT   850
 851  DGVGTKLKIAQQTNRNTSVGIDLVAMCVNDILCNGAEPISFSSYYACGHW   900
 901  QEQLAKGVHSGVQEGARQANSSFIDSHSAALPLLYEPQVYDLAGFALGIA   950
 951  EHTGILPLLAEIQPGDVLIGLPSSGVHSNGFSLVHAVLKRVGLGLHDKAP  1000
1001  FSDKTLGEELLVPTKIYVKALSTLLSRGKHGIKALAHITGGGLSENIPRV  1050
1051  LRKDLAVRLDANKFQLPPVFAWLAAAGNISSTELQRTYNCGLGMVLVVAP  1100
1101  TEVEDVLKELRYPQRAAVVGEVVARKDPKKSQVVVQNFEASLARTQKMLS  1150
1151  QRRKRVAVLISGTGSNLQALIDATRDSAQGIHADVVLVISNKPGVLGLQR  1200
1201  ATQAGIPSLVISHKDFASREVYDAELTRNLKAARVDLICLAGFMRVLSAP  1250
1251  FVREWRGRLVNIHPSLLPKYPGLHVQKQALEAGEKESGCTVHFVDEGVDT  1300
1301  GAIIVQAAVPILPDDDEDSLTQRIHKAEHWAFPRALAMLVNGTALISPEV  1350
1351  SSQ                                                 1353

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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