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

NucPred

Fetching P91660 from www.uniprot.org...

The NucPred score for your sequence is 0.26 (see score help below)

   1  MDKQPVLEPTFDSVSERENTSIEESSLLENNGFDHRNKDESLSVNTSPPL    50
  51  VCRKCFELGHQTENCKQKLTVNSENNSAQNEKEKVLPENPRARSNFISSL   100
 101  CFWYTIPIFRKGYRKTLDSTDLYRPLEEQKSDILGNRLCASWERELKNDG   150
 151  RSPSLVRALLRVFGWQLGFPGLAIFVVELGLRTLQPIFLVKLISYFSGEP   200
 201  DAANAGFYYAVAQIVISALTVMILTPTTFGIHHVCFKMRVAMGSMIFRKA   250
 251  LRLTKGALGDTTSGHVVNLISNDIPRLDSAPYTVHYLWVGPLQVLVITYL   300
 301  MYQEIGISAVFGVLFMLLFMPIQMYLGTRTSAIQLKAAERTDNRIRMVNE   350
 351  IISAIQVLKMYAWEQPFEQMVTHAREKEMNTIRQGQYIRGFDFARRIVLS   400
 401  RVAIFLSLVGYVILGKVFTPEIAFMITAYYNVLLAAMSIYVPSAIIQTAQ   450
 451  FLTSIRRVEQFMQSEELGSSDKSEGPSKDTVPGNPPSNNNEADLLKSAIS   500
 501  IRDLKAKWDPNSPDYTLSGINLEIKPGSVVAVIGLTGSGKSSLIQAILGE   550
 551  LKANSGQLQVNGSLSYTSQESWLFSGTVRQNILFGQPMDSQRYEEVVKKC   600
 601  ALERDFDLLPLRDNTIVGERGATLSGGQKARISLARSVYRKASIYLLDDP   650
 651  LSAVDASVARHLFDQCVRGHLRGSTVVLVTHQEQFLPHVDQIVILANGQI   700
 701  KALGDYESLLKTGLITGLGSLSKTDKAKTEEQEPLNLNSPDNKNEVTPIK   750
 751  ENSEQTVGGSSSGKEHVERQESGGISLALYRKYFQAGGGLVAFLVMLSSS   800
 801  VLAQVAVTGGDYFLTYWVKKESTAAGHGEMEDMESKSMDVYKYTLIIILS   850
 851  VIMNLSSSFLLFNIAKKASIRLHNTIFNRVTRADMHFFSINKHGSILNRF   900
 901  TKDMSQVDEVLPVVLVDVMQIALWLAGIIIVIANVNPLLLVPTLMLSVIF   950
 951  YHLRNLYLKTSRDLKRVEAINRSPVYSHLAASLNGLTTIRALDAQRVLEK  1000
1001  EFDSYQDAHSSAFFMYISTSQAFGYCMNCICVIYISIITLSFFAFPPGNG  1050
1051  ADVGLVITQAMGLIDMVQWGVRQTAELENTMTAVERVVEYESIEPEGMLE  1100
1101  APDDKKPPKTWPEQGEIIFKELNLRYTPNAKAENVLKSLSFVIQPREKVG  1150
1151  IVGRTGAGKSSLINALFRLSYTDGSVLIDTRDTRQMGLHDLRRQISIIPQ  1200
1201  EPVLFSGTMRYNLDPFDEYSDEKLWGCLEEVKLKEVVSDLPDGLASKISE  1250
1251  GGTNFSVGQRQLVCLARAILRENRILVMDEATANVDPQTDGLIQATIRSK  1300
1301  FRDCTVLTIAHRLHTIIDSDKVMVMDAGRVVEFGSPYELMTKSDSKVFHN  1350
1351  LVNQSGRASYEGLLKIAQETFESS                            1374

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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