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

NucPred

Fetching Q7PC82 from www.uniprot.org...

The NucPred score for your sequence is 0.64 (see score help below)

   1  MTMSQTDGVEFASRNTNENGHDDDDQLRSQWVAIERSPTFERITTALFCK    50
  51  RDEKGKKSQRRVMDVSKLDDLDRRLFIDDLIRHVENDNHVLLQKIRKRID   100
 101  EVGIDLPKIEARFSDLFVEAECEVVYGKPIPTLWNAISSKLSRFMCSNQA   150
 151  KKISILKGVSGIIRPKRMTLLLGPPSCGKTTLLLALSGRLDPSLKTRGDI   200
 201  SYNGHLFSEFVPEKTSSYVSQNDLHIPELSVRETLDFSGCFQGTGSRLEM   250
 251  TKEISRREKLKGIVPDPDIDAYMKAASIEGSKTNLQTDYILKILGLTICA   300
 301  DTRVGDASRPGISGGQKRRLTTGEMIVGPIKTLFMDEISNGLDSSTTFQI   350
 351  LSCLQQFARLSEGTILVSLLQPAPETFELFDDLILMGEGKIIYHGPRDFV   400
 401  CSFFEDCGFKCPNRKSVAEFLQEVISRKDQEQYWCHIEKTYCYVSIESFI   450
 451  EKFKKSDLGLELQDRLSKTYDKSQTQKDGLCFRKYSLSNWDMLKACSRRE   500
 501  FLLMKRNSFVYVFKSGLLIFIGFIAMTVYLRTGSTRDSLHANYLMGSLFF   550
 551  SLFKLLADGLPELTLTISRIAVFCKQKELYFYPAWAYAIPSAILKIPISF   600
 601  LESFLWTMLTYYVIGYSPEMGRFIRQFLILFALHLSCISMFRAIAAVFRD   650
 651  FVVATTVGSISIVLLSVFGGFIVRKPSMPSWLEWGFWLSPLSYAEIGLTA   700
 701  NEFFAPRWGKITSENRTLGEQVLDARGLNFGNQSYWNAFGALIGFTLFFN   750
 751  TVFALALTFLKTSQRSRVIVSHEKNTQSSENDSKIASRFKNALPFEPLTF   800
 801  TFQDVQYIIETPQGKKLQLLSGVTGAFKPGVLTALMGVSGAGKTTLLDVL   850
 851  SGRKTFGDIKGQIEVGGYVKVQDTFSRVSGYCEQFDIHSPNLTVQESLKY   900
 901  SAWLRLTSNISSETKCAIVNEVLETIELEEIKDSIVGIPGISGLTTEQRK   950
 951  RLTIAVELVSNPSIIFMDEPTTGLDARAAAIVMRAVKNIAETGRTVVCTI  1000
1001  HQPSIDIFEAFDELILMKNGGKIIYYGPLGQHSSKVIEYFMRIHGVPKLK  1050
1051  ENSNPATWILDITSKSSEDKLGVDLAQMYEESTLFKENKMVIEQTRCTSL  1100
1101  GSERLILSSRYAQTSWEQFKACLWKQHLSYWRNPSYNLTRIIFMSFTCML  1150
1151  CGILFWQKAKEINNQQDLFNVFGSMFTVVLFSGINNCSTVLFSVATERNV  1200
1201  FYRERFSRMYNSWAYSLAQVLVEIPYSLFQSIVYVIIVYPMVGYHWSVFK  1250
1251  VFWSFYSIFCTLLIFNYFGMLLVVVTPNVHIAFTLRSSFYAIVNLFAGYV  1300
1301  MPKPNIPRWWIWMYYLSPTSWVLNGLLTSQYGDMEKEILAFGEKKKVSDF  1350
1351  LEDYFGYRYDSLALVAVVLIAFPILLASLFAFFIGKLNFQKK          1392

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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