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

NucPred

Fetching Q93105 from www.uniprot.org...

The NucPred score for your sequence is 0.58 (see score help below)

   1  MVSLLLFSILVVPVAVVQGDEMKAGQVGPVPKGGVCGSVDVRNSPAHLDR    50
  51  LKDCVVVEGFVHILLIDKYIDSSFENYSFPLLTEITEYLLLFRVNGLKSL   100
 101  RRLFPNLAVIRGDALVGDYAMVIYELMHIEEIGLISLMDITRGGVRIEKN   150
 151  PKLCFANTIDWKAMTVPGTNNYIKDNQKDNVCPICPAESTAVMLPNGSKQ   200
 201  KCPAAPVRGGNKDHKRTLCWNANHCQTICPPECPKACSKTGVCCDAESCL   250
 251  GGCNLPNTSSCSVCRHLSIDPAGKRQCVAKCPPNTFKYHTRCVTRDECYA   300
 301  MKKPISLDSNPDLPDQPFIPHNGSCLMECPLDHELITELNKTRWCRKCSG   350
 351  TCPKRCEGSNIDNIQSAQLLKGCEIIDGSLEIQLRSRGGENIVKELENFL   400
 401  SSITEIKGYLKVVRSYPLLSLGFLKKLKIIHGKGNKVSNSSLYVVENQNL   450
 451  QELFDHNVTIGEGKLFFFNNPMLCTDRIKAVKKYNPGIEIENESQLESNN   500
 501  GDRAACSITELETSLKSIGSETAIIQWAPFTELSDARMLLGYVIYYIEAP   550
 551  YANVTFFDGRDACNTEGWRLDDISDFNMDKETTKILTQLKPYTQYAYYVK   600
 601  TYTLGSEGLGGQSKIKYFTTAPGTPSVVRDVEVSVNKNMLTVKWLPPLKM   650
 651  NGRLKEYEVFIELNADDNEQLMLRDYCEDDKLRDIVPETPTSAPPPKTSI   700
 701  CTADQCRNYCKAPTSGGSTGTIDVTDKENQITFEDQLHNYVYIKNPLLRD   750
 751  KTTRRKRSTNLLFPNNTENKKNDTTDRRTEKVKDEPYYQYIFNATNETSI   800
 801  TFPLSYFNHYSLYVFKIRACRHPGDPPAPSVRLVDVELACGNEVFENFRT   850
 851  PKKEGADDIPPESILIEEQSNNTQRQIRVQWKEPSKPNGPIVKFVVKYQR   900
 901  VDLESVSSTDICIRYSSFNQTRGALLTKLEPGNYSIRVMATTIAGDGAPS   950
 951  AARYVLIAKDDSMGTTLIWLGTLIVIFLCSVGFVAFYWYKYRYMSKQIRM  1000
1001  YPEVNPDYAGVQYKVDDWEVERNHIIQLEELGQGSFGMVYKGILTQLRGE  1050
1051  KCNQPCAIKTVNESATAREKDSFLLEASVMKQFNTHHVVRLLGVVSQGDP  1100
1101  TLVIMELMANGDLKSYLRRHRPDYENGEDPSPQPPTLRQIIQMAIEIADG  1150
1151  MAYLSAKKFVHRDLAARNCMVADDMTVKIGDFGMTRDIYETDYYRKGTKG  1200
1201  FLPVRWMAPESLKDGIFSSSSDVFSYGVVLWEMATLASQPYQGLTNDQVL  1250
1251  RYVIDGGVMERPENCPDNLYNLMRRCWQHRPTARPTFMEIISELLPDASP  1300
1301  HFQDVAFYNSQDALDMLRGQHQTVIIDEATTPLRPGDDHDEEPGEDDDLV  1350
1351  GHGEGHIGDVGTDDEFSMEMTNSHLVRNNGPMATIRSPHSPLR         1393

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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