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

NucPred

Fetching P24062 from www.uniprot.org...

The NucPred score for your sequence is 0.44 (see score help below)

   1  MKSGSGGGSPTSLWGLVFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLE    50
  51  NCTVIEGFLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLF   100
 101  PNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLC   150
 151  YLSTIDWSLILDAVSNNYIVGNKPPKECGDLCPGTLEEKPMCEKTTINNE   200
 201  YNYRCWTTNRCQKMCPSVCGKRACTENNECCHPECLGSCHTPDDNTTCVA   250
 251  CRHYYYKGVCVPACPPGTYRFEGWRCVDRDFCANIPNAESSDSDGFVIHD   300
 301  GECMQECPSGFIRNSTQSMYCIPCEGPCPKVCGDEEKKTKTIDSVTSAQM   350
 351  LQGCTILKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSL   400
 401  SFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWNHRNLTVRSGKMYFA   450
 451  FNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLRFTSTT   500
 501  TWKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSW   550
 551  NMVDVDLPPNKEGEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKS   600
 601  EILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPTLPNGNLSYYIVRWQ   650
 651  RQPQDGYLFRHNYCSKDKIPIRKYADGTIDVEEVTENPKTEVCGGDKGPC   700
 701  CACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERRRRDVLQVANTT   750
 751  MSSRSRNTTVADTYNITDPEEFETEYPFFESRVDNKERTVISNLRPFTLY   800
 801  RIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIF   850
 851  LKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPG   900
 901  NYTARIQATSLSGNGSWTDPVFFYVPAKTTYENFMHLIIALPVAILLIVG   950
 951  GLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKI  1000
1001  TMNRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLN  1050
1051  EASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEVE  1100
1101  NNLVLIPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTV  1150
1151  KIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTHSDVWSFG  1200
1201  VVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCW  1250
1251  QYNPKMRPSFLEIIGSIKDEMEPSFQEVSFYYSEENKPPEPEELEMELEL  1300
1301  EPENMESVPLDPSASSASLPLPERHSGHKAENGPGVLVLRASFDERQPYA  1350
1351  HMNGGRANERALPLPQSSTC                                1370

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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