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

NucPred

Fetching P15208 from www.uniprot.org...

The NucPred score for your sequence is 0.66 (see score help below)

   1  MGFGRGCETTAVPLLVAVAALLVGTAGHLYPGEVCPGMDIRNNLTRLHEL    50
  51  ENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLK   100
 101  DLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNN   150
 151  ELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDVCPGTAKGKTNCPATV   200
 201  INGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHKECLGNCSEPDDPT   250
 251  KCVACRNFYLDGQCVETCPPPYYHFQDWRCVNFSFCQDLHFKCRNSRKPG   300
 301  CHQYVIHNNKCIPECPSGYTMNSSNLMCTPCLGPCPKVCQILEGEKTIDS   350
 351  VTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGFLKIRRS   400
 401  YALVSLSFFRKLHLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQ   450
 451  GKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELL   500
 501  KFSFIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDA   550
 551  CGSNSWTVVDIDPPQRSNDPKSQTPSHPGWLMRGLKPWTQYAIFVKTLVT   600
 601  FSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDP   650
 651  NGNITHYLVYWERQAEDSELFELDYCLKGLKLPSRTWSPPFESDDSQKHN   700
 701  QSEYDDSASECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRPSRK   750
 751  RRSLEEVGNVTATTLTLPDFPNVSSTIVPTSQEEHRPFEKVVNKESLVIS   800
 801  GLRHFTGYRIELQACNQDSPDERCSVAAYVSARTMPEAKADDIVGPVTHE   850
 851  IFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERG   900
 901  CRLRGLSPGNYSVRVRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIG   950
 951  PLIFVFLFSVVIGSIYLFLRKRQPDGPMGPLYASSNPEYLSASDVFPSSV  1000
1001  YVPDEWEVPREKITLLRELGQGSFGMVYEGNAKDIIKGEAETRVAVKTVN  1050
1051  ESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGD  1100
1101  LKSHLRSLRPDAENNPGRPPPTLQEMIQMTAEIADGMAYLNAKKFVHRDL  1150
1151  AARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKD  1200
1201  GVFTASSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDPPDN  1250
1251  CPERLTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFYSEENK  1300
1301  APESEELEMEFEDMENVPLDRSSHCQREEAGGREGGSSLSIKRTYDEHIP  1350
1351  YTHMNGGKKNGRVLTLPRSNPS                              1372

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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