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

NucPred

Fetching Q852M1 from www.uniprot.org...

The NucPred score for your sequence is 0.14 (see score help below)

   1  MGSEAAAARPVVVTVNGERYEAVGVDPSTTLLEFLRTRTPVRGPKLGCGE    50
  51  GGCGACVVVVSKYDAVADEVTEFSASSCLTLLGSLHHCAVTTSEGIGNSR   100
 101  DGFHAVQRRLSGFHASQCGFCTPGMCMSIYSALAKADRCSSRPSPPPGFS   150
 151  KLTAAEAEKAVSGNLCRCTGYRPIVDACKSFAADVDLEDLGLNAFWKKGA   200
 201  DDERADVGKLPAYSGGAAVCTFPEFLKSEIRSSMGQANGGAPAVAVTGDG   250
 251  WFHPKSVEEFHRLFDSNLFDERSVKIVASNTGSGVYKDQDLHDKYINISQ   300
 301  ILELSAINRSSKGVEIGAVVSISKAIEILSDGGAVFRKIADHLSKVASSF   350
 351  VQNTATIGGNIIMAQRLSFPSDIATVLLAAGSTVTIQVAAKRMCITLEEF   400
 401  LKQPPCDSRTLLVSISIPDWGSDDGITFESFRAAPRPLGNAVSYVNSAFL   450
 451  ARSSVDGSSGSHLIEDVCLAFGAFGAEHAIRAREVEEFLKGKLVSAPVIL   500
 501  EAVRLLKGVVSPAEGTTHPEYRVSLAVSYLFRFLTSLANGLDEPENANVP   550
 551  NGSCTNGTANGSANSSPEKHSNVDSSDLPIKSRQEMVFSDEYKPVGKPIE   600
 601  KTGAELQASGEAVYVDDIPAPKDCLYGAFIYSTHPHAHIKDINFRSSLAS   650
 651  QKVITVITAKDIPTGGENIGSCFPMLGDEALFVHPVSEFAGQNIGVVIAE   700
 701  TQKYAYMAAKQAVIEYSTENLQPPILTIEDAVQHNSYFPVPPFLAPTPIG   750
 751  DFNQAMSEADHKIIDGEVKLESQYYFYMETQTALAIPDEDNCITLYVSAQ   800
 801  LPEITQNTVARCLGIPYHNVRIITRRVGGGFGGKAMKAIHVAAACAVAAF   850
 851  KLRRPVRMYLDRKTDMIMAGGRHPMKVKYSVGFKSDGKITGLHFDLGMNG   900
 901  GISPDCSPVLPVAIVGALKKYNWGALSFDIKVCKTNVSSKSAMRAPGDAQ   950
 951  GSFIAEAIVEHIASTLSVDTNAIRRKNLHDFESLKVFYGNSAGDPSTYSL  1000
1001  VTIFDKLASSPEYQQRAAMVEHFNAGNRWKKRGISCVPITYDVRLRPTPG  1050
1051  KVSIMNDGSIAVEVGGVEIGQGLWTKVKQMTAFALGQLCDDGGEGLIDKV  1100
1101  RVIQADTLSMIQGGFTGGSTTSETSCEAVRKSCAALVERLKPIKEKAGTP  1150
1151  PWKSLIAQASMASVKLTEHAYWTPDPTFTSYLNYGAAISEVEVDVLTGET  1200
1201  TILRSDLVYDCGQSLNPAVDLGQVEGAFVQGIGFFTNEEYTTNSDGLVIN  1250
1251  DGTWTYKIPTVDTIPKQFNVELINSARDHKRVLSSKASGEPPLLLASSVH  1300
1301  CAMREAIRAARKEFAGAGGSPLTFQMDVPATMPIVKELCGLDVVERYLES  1350
1351  FAAKA                                               1355

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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