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

NucPred

Fetching P38550 from www.uniprot.org...

The NucPred score for your sequence is 0.83 (see score help below)

   1  MEVLMAERANLVFHNKAINGTAMKRLISRLIDHFGMAYTSHILDQVKTLG    50
  51  FQQATATSISLGIDDLLTIPSKGWLVQDAEQQSLILEKHHHYGNVHAVEK   100
 101  LRQSIEIWYATSEYLRQEMNPNFRMTDPFNPVHIMSFSGARGNASQVHQL   150
 151  VGMRGLMSDPQGQMIDLPIQSNLREGLSLTEYIISCYGARKGVVDTAVRT   200
 201  SDAGYLTRRLVEVVQHIVVRRTDCGTARGISVSPRNGMMPERIFIQTLIG   250
 251  RVLADDIYMGPRCIATRNQDIGIGLVNRFITFRAQPISIRTPFTCRSTSW   300
 301  ICRLCYGRSPTHGDLVELGEAVGIIAGQSIGEPGTQLTLRTFHTGGVFTG   350
 351  GTAEHVRAPSNGKIKFNEDLVHPTRTRHGHPAFLCSIDLYVTIESEDILH   400
 401  NVNIPPKSLLLVQNDQYVESEQVIAEIRAGISTLNFKEKVRKHIYSDSDG   450
 451  EMHWSTDVYHAPEFTYGNVHLLPKTSHLWILLGRPCRSSLVYLSIHKDQD   500
 501  QMNAHFLSGKRRYTSNLSVTNDQARQKLFSSDFSGKKEDRIPDYSDLNRI   550
 551  ICAGQYNLVYSPILHENSDLLSKRRRNKFIIPLHSIQELENELMPCSGIS   600
 601  IEIPVNGIFRRNSILAYFDDPRYRRKSSGIIKYGTVETHSVIKKEDLLEY   650
 651  RGVKEFRPKYQMKVDRFFFIPEEVHILPGSSSIMVRNNSIVGVDTQITLN   700
 701  LRSRVGGLVRVERKKKRIELKIFSGDIHFPGETDKISRHTGVLIPPGTGK   750
 751  RNSKESKKVKNWIYVQRITPSKKKFFVLVRPVVTYEITDGINLATLFPPD   800
 801  PLQERDNVQLRIVNYILYGNGKPIRGISDTSIQLVRTCLVLNWNQDKKSS   850
 851  SCEEARASFVEIRTNGLIRHFLRINLVKSPISYIGKRNDPSGSGLLSDNG   900
 901  SDCTNINPFSSIYSYSKAKIQQSINQPQGTIHTLLNRNKECQSLIILSAA   950
 951  NCSRMGPFKDVKYHSVIKKSIKKDPLIPIRNSLGPLGTSLPIENFYSSYH  1000
1001  LITHNQILVTNYLQLDNLKQTFQVIKFKYYLMDENGKIFNPDPCRNIILN  1050
1051  PFNLNWYFLHHNYCEETSKIISLGQFICENVCIAKNGPPLKSGQVILVQV  1100
1101  DSIVIRSAKPYLATPGATVHGHYGETLYEGDTLVTFIYEKSRSGDITQGL  1150
1151  PKVEQVLEVRSVDSISMNLEKRIEGWNKCITRILGIPWGFLIGAELTIAQ  1200
1201  SRISLVNKIQQVYRSQGVQIHNRHLEIIVRQITSKVLVSEDGMSNVFSPG  1250
1251  ELIGLLRAERMGRALEEAICYRVVLLGITRASLNTQSFISEASFQETARV  1300
1301  LAKAALRGRIDWLKGLKENVVLGGVIPVGTGFKGLVHPSKQHNNIPLETK  1350
1351  KKNLFEGEMRDILFHHKKLFDSCLSKNFHDIPEQSFIGFNDS          1392

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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