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

NucPred

Fetching Q852M2 from www.uniprot.org...

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

   1  MGSEAAAAARAVVVAVNGERYEAVGVDPSTTLLEFLRTRTPVRGPKLGCG    50
  51  EGGCGACVVVVSKYDAVADEVTEFSASSCLTLLGSLHHCAVTTSEGIGNS   100
 101  RDGFHAVQRRLSGFHASQCGFCTPGMCMSIYSALAKADKASGRPAPPTGF   150
 151  SKITAAEAEKAVSGNLCRCTGYRPIVDACKSFAADVDLEDLGLNAFWKKG   200
 201  VDDEHADINKLPAYSGGAAVCTFPEFLKSEIRSSMGQANGDTSAVVVTGD   250
 251  GWFHPKSVEEFHRLFDSNLFDERSVKIVASNTGSGVYKDQDLHDKYINIS   300
 301  QIPELSAINRSSKGVEIGAVVSISQAIDILSDGGAVFRKIADHLSKVASP   350
 351  FVRNTATIGGNIIMAQRLSFSSDIATVLLAAGSTVTIQVAAKRMCITLEE   400
 401  FLKQPPCDSRTLLVSISIPDWGSDDGITFQTFRAAPRPLGNAVSYVNSAF   450
 451  LARSSVDGSSGSHLIEDVCLAFGPFGAKHAIRAREVEKFLKGKLVSAPVI   500
 501  LEAVRLLKGVVSPAEGTTHPEYRVSLAVSYLFKFLSSLTNGLDEPENANV   550
 551  PNGSFTNGTANGIVDSSPEKHSNVDSSYLPIKSRQEMVFSDEYRPIGKPI   600
 601  EKTGAELQASGEAVYVDDISAPKDCLYGAFIYSTHPHAHIKGVNFRSSLA   650
 651  SQKVITVITLKDIPTNGKNIGSCSPMLGDEALFVDPVSEFAGQNIGVVIA   700
 701  ETQKYAYMAAKQSVIEYSTENLQPPILTVEDAVQHNSYFQVPPFLAPTPI   750
 751  GEFNQAMSEADHKIIDGEVKLESQYYFYMETQTALAIPDEDNCITLYVSA   800
 801  QLPEITQNTVARCLGIPYHNVRIITRRVGGGFGGKAMKAIHVATACAVAA   850
 851  FKLRRPVRMYLDRKTDMIMAGGRHPMKVKYSVGFKSDGKITGLHVDLRIN   900
 901  CGISPDCSPALPVAIVGALKKYNWGALSFDIKLCKTNVSSKSAMRAPGDA   950
 951  QGSFIAEAIVEHIASTLSVDTNAIRRKNLHDFESLKVFYGNSAGDPSTYS  1000
1001  LVTIFDKLASSPEYQQRAAVVEHFNAGSRWKKRGISCVPITYDVRLRPSP  1050
1051  GKVSIMNDGSIAVEVGGVEIGQGLWTKVKQMTAFALGQLCDDGGEGLLDK  1100
1101  VRVIQADTLSMIQGGFTGGSTTSETSCEAVRKSCAALVERLKPIKEKAGT  1150
1151  LPWKSLIAQASMASVKLTEHAYWTPDPTFTSYLNYGAAISEVEVDVLTGE  1200
1201  TTILRSDLVYDCGQSLNPAVDLGQVEGAFVQGIGFFTNEEYTTNSDGLVI  1250
1251  NDGTWTYKIPTVDTIPKQFNVELINSARDHKRVLSSKASGEPPLLLASSV  1300
1301  HCAMREAIRAARKEFAGAGGSSLTFQMDVPATMPIVKELCGLDVVERDLE  1350
1351  SFAAKA                                              1356

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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