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

NucPred

Fetching O08774 from www.uniprot.org...

The NucPred score for your sequence is 0.81 (see score help below)

   1  MYRAGEPGKRQSGPAPPRVRSVEVARGRAGYGFTLSGQAPCVLSCVMRGS    50
  51  PADFVGLRAGDQILAINEINVKKASHEDVVKLIGKCSGVLRMVISEGSSH   100
 101  VEPSSSDEEGGLCEGKGWLRPKLDSKALGINRAERVVEEVQSGGIFNMIF   150
 151  ESPSLCASGSEPLKLKQRSLSESAALRLDVGQDSLCTPHPSMLSKEEISK   200
 201  VINDDSVFTVGLDNHDDFGLDASILNVAMVVGYLGSIELPSTSSNLEHDS   250
 251  LQAIRGCMRRLRAEQKIHSLVTMKVMHDCVQLVTDRAGVVAEYPAEKLAF   300
 301  SAVCPDDRRFFGLVTMQTNDDGCLAQEDEGALRTSCHVFMVDPDLFHHKI   350
 351  HQGIARRFGFACTADPDTSGCLEFPASSLPVLQFISVLYRDMGELIEGVR   400
 401  ARAFLDGDADAHQNNSTSSNSDSGIGNFNQEEKSNRVLVVDLGGGSSRHG   450
 451  QGSSPGWESVSGRGSQPWSAPWNGTFCHDSEAGSPLETSPNTDRFWDLTK   500
 501  HSGPVFHMEVPPATLRSSIPPSKRGATGSSCGFNQRWLPVHVLQEWQCGH   550
 551  ASDQESYTDSTDGWSSVNCGTLPPPMSKIPADRYRVEGSFAQAPLSTQKR   600
 601  DWSRKAFGMQNLFGPHRNVRKTKEDKKSSKLGRGVALAQTSQRTSARRSF   650
 651  GRSRRFSLTRSLDDLESATVSDGELTGADLKDCISNNSLSSNASLPSVQS   700
 701  CRRLRERRVASWAVSFERLLQDPVGVRYFSDFLRKEFSEENILFWQACEC   750
 751  FSHVPAHDKKELSYRAREIFSKFLCSKATTPVNIDSQAQLADDILNAPHP   800
 801  DMFKEQQLQIFNLMKFDSYTRFLKSQLYQECVLAEVEGRTLPDSQQVPSS   850
 851  PASKHSISSDHSNVSTPKKLSGKSKSGRSLNEDVGEEDSEKKRKGAFFSW   900
 901  SRSRSTGRSQKKKDHGDHAHDALHANGGLCRRESQGSVSSAGSLDLSEAC   950
 951  RTSALERDKAAKHCCVHLPDGTSCVVAVKSGFSIKEILSGLCERHGINGA  1000
1001  AVDLFLVGGDKPLVLHQDSSILATRDLRLGKRTLFRLDLVPINRSVGLKA  1050
1051  KPTKPVTEVLRPVVAKYGLDLGSLLVRLSGEKEPLDLGAPISSLDGQRVI  1100
1101  LEERDPSRGKVSTEKQKGAPVKQSSAVNSSPRNHSAMGEERTLGKSNSIK  1150
1151  IRGENGKSARDPRLSKREESIAKIGKKKYQKINLDEAEEFFELISKAQSN  1200
1201  RADDQRGLLRKEDLVLPEFLRLPPGSSELALSSPPPVKGFSKRAVTSHGQ  1250
1251  EGAVQTEESYSDSPATSPASAQSPCSAYSPGSAHSPGSAHSPGSAHSTPG  1300
1301  PPGTAQPGEKPTKPSCISTVQEGTTQAWRRLSPELEAGGIQTVEEEQVAD  1350
1351  LTLMGEGDISSPNSTLLPPPPLPQDTPGPTRPGTSRF               1387

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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