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

NucPred

Fetching P47104 from www.uniprot.org...

The NucPred score for your sequence is 0.72 (see score help below)

   1  MSLNFLPGRPNATPQTACQATWQNHTIFAYCSGNNLIILTNKFTRLQTIY    50
  51  TQSDCTAVDINSQNGFIALSFHNRVLIYKPIHQIMQNPKWTQCCQLFHDD   100
 101  TPVNCLRWSSDNELAIGSDFLSFWKIKDNFGVYQPILQWNQKQPKPVYNV   150
 151  IISQDSQLIVSIGKYDCNAKLWKRVSIVGEQAIFNLTMLPHPKPITAMRW   200
 201  KKEPDQVSKNNTASHALYTLCEDKVLRIWSCFEMEKNHTVQIWGEVPLSP   250
 251  TQKFCVIIDNWIIRQTLSVKDSEIFDISDSDIVILGSMTGEMEVLALNNL   300
 301  SQDPPKPMTKKTISHKKVKKATMLNDTRYLYLPEIQPYDNVKGKLSFLVH   350
 351  DLQGVIRHLLIDILQLINNKTEDLSAALEHKFTGHNKSVQKLVRSSDGEA   400
 401  LLTTSRFSENGVWYPQKLNHGVSLRLQNTIQTESPIKFAVVHELGKQVIC   450
 451  LLENGALQAWECPTNRKEDSEQKQSYLRVETRLKEEKKIHPIVMLNTPEP   500
 501  KHSHERHFTALIFSDGSIKAFEVSLTRGIFEVKSDSLDIDGDDIYKISII   550
 551  DPVHQTFVSNRPLISLITKKGLTRTYKAIVNYNDRHVQWIKACEINTGIM   600
 601  NCTCIRGSSTGKLCIVNSTGKVMSLWDLNRGVLEYEETFHNPIEDIDWTS   650
 651  TEYGQSIVSIGFTGYALLYTQLRYDYTNNTPSYLPIEKIDITAHTAHNIG   700
 701  DSVWMKNGTFVVASGNQFYIKDKSLDLTDPFTYQSIGSRKILSNDILHLS   750
 751  SVLNGPLPVYHPQFLIQAIYANKLQLVKELLLRLFLALRKLDFESQDVSN   800
 801  LDSNLGMDPLKYFIAKDRDYPVESFPDPYPCFNKTVSLALTEQLTKTTLP   850
 851  YLTRHQQITLITVIEAVDEVTKNENIVDYNGVRFLLGVKLFLSHKNIQKS   900
 901  ILMRDVSWALHSDNKEILLSSIDRHITSWNRAREYRIAYWIKEQDLVKKF   950
 951  EDIAKYEFSKDDKRDPSRCAIFYLALKKKQILLSLWKMAIGHPEQQKMVR  1000
1001  FISNDFTVPRWRTAALKNAFVLLSKHRYMDAAVFFLLTDSLKDCVNVLCK  1050
1051  QVHDMDLAIGVCRVYEGDNGPVLGELLTAQMLPETIKENDRWKASFIYWK  1100
1101  LRKQEVAIKALLTAPIDLENNSSIVDKEVCVNRSFLVEDPALLYLYNHLR  1150
1151  NRNLKYFIGSLNVEAKIECTLILRVTDILCRMGCNYLAVSLVKNWKFIER  1200
1201  NSIPVQKLLKSPTKDRAYSAIGAMASEPISTARMRPSLFDKFGSPSASDI  1250
1251  ESPNPKLPNSLLDDFLQPPPNSTSSNSLAQSSSSAPRSILDEFVSPSYSQ  1300
1301  HKENLTPKAPNDSVGETDNSENRKDKLSKDILDDLSSQKPQKPKKSAITK  1350
1351  NLLDDFV                                             1357

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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