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

NucPred

Fetching Q9HAR2 from www.uniprot.org...

The NucPred score for your sequence is 0.61 (see score help below)

   1  MWPSQLLIFMMLLAPIIHAFSRAPIPMAVVRRELSCESYPIELRCPGTDV    50
  51  IMIESANYGRTDDKICDSDPAQMENIRCYLPDAYKIMSQRCNNRTQCAVV   100
 101  AGPDVFPDPCPGTYKYLEVQYECVPYKVEQKVFLCPGLLKGVYQSEHLFE   150
 151  SDHQSGAWCKDPLQASDKIYYMPWTPYRTDTLTEYSSKDDFIAGRPTTTY   200
 201  KLPHRVDGTGFVVYDGALFFNKERTRNIVKFDLRTRIKSGEAIIANANYH   250
 251  DTSPYRWGGKSDIDLAVDENGLWVIYATEQNNGKIVISQLNPYTLRIEGT   300
 301  WDTAYDKRSASNAFMICGILYVVKSVYEDDDNEATGNKIDYIYNTDQSKD   350
 351  SLVDVPFPNSYQYIAAVDYNPRDNLLYVWNNYHVVKYSLDFGPLDSRSGQ   400
 401  AHHGQVSYISPPIHLDSELERPSVKDISTTGPLGMGSTTTSTTLRTTTLS   450
 451  PGRSTTPSVSGRRNRSTSTPSPAVEVLDDMTTHLPSASSQIPALEESCEA   500
 501  VEAREIMWFKTRQGQIAKQPCPAGTIGVSTYLCLAPDGIWDPQGPDLSNC   550
 551  SSPWVNHITQKLKSGETAANIARELAEQTRNHLNAGDITYSVRAMDQLVG   600
 601  LLDVQLRNLTPGGKDSAARSLNKAMVETVNNLLQPQALNAWRDLTTSDQL   650
 651  RAATMLLHTVEESAFVLADNLLKTDIVRENTDNIKLEVARLSTEGNLEDL   700
 701  KFPENMGHGSTIQLSANTLKQNGRNGEIRVAFVLYNNLGPYLSTENASMK   750
 751  LGTEALSTNHSVIVNSPVITAAINKEFSNKVYLADPVVFTVKHIKQSEEN   800
 801  FNPNCSFWSYSKRTMTGYWSTQGCRLLTTNKTHTTCSCNHLTNFAVLMAH   850
 851  VEVKHSDAVHDLLLDVITWVGILLSLVCLLICIFTFCFFRGLQSDRNTIH   900
 901  KNLCISLFVAELLFLIGINRTDQPIACAVFAALLHFFFLAAFTWMFLEGV   950
 951  QLYIMLVEVFESEHSRRKYFYLVGYGMPALIVAVSAAVDYRSYGTDKVCW  1000
1001  LRLDTYFIWSFIGPATLIIMLNVIFLGIALYKMFHHTAILKPESGCLDNI  1050
1051  KSWVIGAIALLCLLGLTWAFGLMYINESTVIMAYLFTIFNSLQGMFIFIF  1100
1101  HCVLQKKVRKEYGKCLRTHCCSGKSTESSIGSGKTSGSRTPGRYSTGSQS  1150
1151  RIRRMWNDTVRKQSESSFITGDINSSASLNREGLLNNARDTSVMDTLPLN  1200
1201  GNHGNSYSIASGEYLSNCVQIIDRGYNHNETALEKKILKELTSNYIPSYL  1250
1251  NNHERSSEQNRNLMNKLVNNLGSGREDDAIVLDDATSFNHEESLGLELIH  1300
1301  EESDAPLLPPRVYSTENHQPHHYTRRRIPQDHSESFFPLLTNEHTEDLQS  1350
1351  PHRDSLYTSMPTLAGVAATESVTTSTQTEPPPAKCGDAEDVYYKSMPNLG  1400
1401  SRNHVHQLHTYYQLGRGSSDGFIVPPNKDGTPPEGSSKGPAHLVTSL     1447

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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