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

NucPred

Fetching Q12572 from www.uniprot.org...

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

   1  MTDFWLNYLDNPTLSVLPHDFLKPANNKSVEGTYTFNIDNGSTDFKFGLA    50
  51  VFAALVYRLTGDEDIVIATDESANTPEFIVRLNLTPELTFQELVSKITKE   100
 101  YENNISQINYKALSEVSHRIKEAKGLDENPGLFRLSYQHAHSNQQLNTTV   150
 151  EGSIRDLAIYTDGTKFTIYYNALLYSHERVVICGEQFAQLTTVSGDTDTV   200
 201  IAEVFLITDFHKKNLPDPTIDLDWSGYRGAIQEIFMDNANKHPDRTCVVE   250
 251  TVSFLESNSKTRNFSYHKLIKLLIVVGNYLKETGIKKGDIVMIYAYRGVD   300
 301  LMIAVMGVLKAGATFSVIDPAYPPARQNIYLSVAKPKGLIGLEKAGTLDQ   350
 351  LVVDYISNELDVVSTIPQLKVQDDGTLVGGKLEGADNDCLNDYQKFKDQP   400
 401  AGVIVGPDSRPTLSFTSGSEGIPKGVLGRHYSLAYYFPWMAKRFRLSEKD   450
 451  KFTILSGIAHDPIQRDMFTPLFLGAQLLVPTADDIGTPGKLADWMAKYGA   500
 501  TVTHLTLAMGQLLSAQATTAIPSLHAFFVGDILTKRDCLRLQSLAENVFI   550
 551  VNMLWSLSQTQRSVSYFEIKSRKADPTYLKNLKAVMPAGTGMHNVQLLVV   600
 601  NRNDRSQTCGVGEVGEIYVRAAGLAEGYRGLPDLNAAKFITNWYVNPDKW   650
 651  IEQDEANKKSSETSERTWSVKPRDRMYRSGDLGRYFSDGNVECCGRADDQ   700
 701  VKIRGFRIELGEIDTHLSQHPLVRENVTLVRRDKNEEPTLISYIVPKDSP   750
 751  ELKTFFADVDFPLKKSNDPIVKGLVAYRELIKDIKGYLKKKLASYAIPTI   800
 801  IVPLVKLPLNPNGKVDKPKLPFPDTAQLAAVAKLSVSSHDAQAAEEENLT   850
 851  KLEEQIRDLWLDVLPNRPATISKDDSFFDLGSHSILGTRIFTYEQKLNVE   900
 901  IPLVSFKGDQRRPRFPIGLSRYNYSRREQRCRRFLKAKTYTMRRSKELSK   950
 951  ELSKSALLESYSSLKQLPSGSVNVFVTGATGFLGSFIVRDLLTARNKNLD  1000
1001  IKVYAHVRASSKEAGLQRLRQTGITYGIWDENWAEKIEIVLGDLSKEKFG  1050
1051  LDNSQWSDLTNSIDVLFTMVLCHWVYPYSQLRMLNVIGTINVFNMAGEVK  1100
1101  LKFFSFVSSTSALDTDYFVNLSDELLAQGKNGISEADDLQGSAKGLGNGY  1150
1151  GQSKWAAEYIIRRAGERGLKGCITRPGYVAGFSKTGASNTDDFLLRMLKG  1200
1201  SAELGLYPDITNNVNMVPVDHVARVVTATALNPPSSEELTVAHVTGHPRI  1250
1251  LFNNFLGCLKAYGYEINPADYPVWTSALEKFVIEESKESALFPLLHFVLD  1300
1301  NLPQDTKAPELDDSNAAKSLKQDSKYTGEDFSAGKGVDLDQTGVYISYLI  1350
1351  KIGFLPKPTGTGEKKLPEVEISDESLKLISGGAGARGSAAK           1391

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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