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

NucPred

Fetching P38972 from www.uniprot.org...

The NucPred score for your sequence is 0.19 (see score help below)

   1  MTDYILPGPKALSQFRVDNLIKDINSYTNSTSVINELRSCYIHYVNGIAQ    50
  51  NLSEQDTKLLEVLLTYDSALDIANDPLARQLNDAVANNLPSSALGEDTYL   100
 101  IRVVPRSGTISPWSSKATNIAHVCGLQDKVQRIERGLALLIKTVPGFPLL   150
 151  ENLNDISLKCVYDRMTQQLYLTEPPNTMSIFTHEEPKPLVHVPLTPKDTK   200
 201  QSPKDILSKANTELGLALDSGEMEYLIHAFVETMKRDPTDVELFMFAQVN   250
 251  SEHCRHKIFNADWTIDGIKQQFTLFQMIRNTHKLNPEYTISAYSDNAAVL   300
 301  DSENDAFFFAPNSTTKEWTSTKERIPLLIKVETHNHPTAVSPFPGAATGS   350
 351  GGEIRDEGATGRGSKTKCGLSGFSVSDLLIPGNEQPWELNIGKPYHIASA   400
 401  LDIMIEAPLGSAAFNNEFGRPCINGYFRTLTTKVLNHQGKEEIRGFHKPI   450
 451  MIAGGFGTVRPQFALKNTPITPGSCLIVLGGQSMLIGLGGGAASSVASGE   500
 501  GSADLDFASVQRGNPEMERRCQQVIDACVALGNNNPIQSIHDVGAGGLSN   550
 551  ALPELVHDNDLGAKFDIRKVLSLEPGMSPMEIWCNESQERYVLGVSPQDL   600
 601  SIFEEICKRERAPFAVVGHATAEQKLIVEDPLLKTTPIDLEMPILFGKPP   650
 651  KMSRETITEALNLPEANLSEIPSLQDAIQRVLNLPSVGSKSFLITIGDRS   700
 701  VTGLIDRDQFVGPWQVPVADVGVTGTSLGETIISTGEAMAMGEKPVNALI   750
 751  SASASAKLSVAESLLNIFAADVKSLNHIKLSANWMSPASHQGEGSKLYEA   800
 801  VQALGLDLCPALGVAIPVGKDSMSMKMKWDDKEVTAPLSLNITAFAPVFN   850
 851  TSKTWTPLLNRNTDDSVLVLVDLSAKQETKSLGASALLQVYNQVGNKSPT   900
 901  VYDNAILKGFLESLIQLHQQKEDIVLAYHDRSDGGLLITLLEMAFASRCG   950
 951  LEINIDGGDLESQLTNLFNEELGAVFQISAKNLSKFEKILNENGVAKEYI  1000
1001  SIVGKPSFQSQEIKIINSTTNDVIYANSRSELEQTWSKTSYEMQKLRDNP  1050
1051  KTAEEEFASITDDRDPGLQYALTYNPADDMKIGLELSSQRPKVAILREQG  1100
1101  VNGQMEMAWCFQQAGFNSVDVTMTDLLEGRFHLDDFIGLAACGGFSYGDV  1150
1151  LGAGAGWAKSVLYHEGVRSQFSKFFNERQDTFAFGACNGCQFLSRLKDII  1200
1201  PGCENWPSFERNVSEQYEARVCMVQISQEKDNSSEESVFLNGMAGSKLPI  1250
1251  AVAHGEGKATFSKSAEQLEKFEKDGLCCIRYVDNYGNVTERFPFNPNGST  1300
1301  NGIAGIKSPNGRVLAMMPHPERVCRLEANSWYPEGKYEEWGGYGPWIRLF  1350
1351  RSARRWVG                                            1358

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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