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

NucPred

Fetching Q9ULL1 from www.uniprot.org...

The NucPred score for your sequence is 0.98 (see score help below)

   1  MELSDSDRPVSFGSTSSSASSRDSHGSFGSRMTLVSNSHMGLFNQDKEVG    50
  51  AIKLELIPARPFSSSELQRDNPATGQQNADEGSERPPRAQWRVDSNGAPK   100
 101  TIADSATSPKLLYVDRVVQEILETERTYVQDLKSIVEDYLDCIRDQTKLP   150
 151  LGTEERSALFGNIQDIYHFNSELLQDLENCENDPVAIAECFVSKSEEFHI   200
 201  YTQYCTNYPRSVAVLTECMRNKILAKFFRERQETLKHSLPLGSYLLKPVQ   250
 251  RILKYHLLLHEIENHLDKDTEGYDVVLDAIDTMQRVAWHINDMKRKHEHA   300
 301  VRLQEIQSLLTNWKGPDLTSYGELVLEGTFRIQRAKNERTLFLFDKLLLI   350
 351  TKKRDDTFTYKAHILCGNLMLVEVIPKEPLSFSVFHYKNPKLQHTVQAKS   400
 401  QQDKRLWVLHLKRLILENHAAKIPAKAKQAILEMDAIHHPGFCYSPEGGT   450
 451  KALFGSKEGSAPYRLRRKSEPSSRSHKVLKTSETAQDIQKVSREEGSPQL   500
 501  SSARPSPAQRNSQPSSSTMISVLRAGGALRNIWTDHQIRQALFPSRRSPQ   550
 551  ENEDDEDDYQMFVPSFSSSDLNSTRLCEDSTSSRPCSWHMGQMESTETSS   600
 601  SGHRIVRRASSAGESNTCPPEIGTSDRTRELQNSPKTEGQEEMTPFGSSI   650
 651  ELTIDDIDHVYDNISYEDLKLMVAKREEAESTPSKSARDSVRPKSTPELA   700
 701  FTKRQAGHSKGSLYAQTDGTLSGGEASSQSTHELQAVEENIYDTIGLPDP   750
 751  PSLGFKCSSLKRAKRSTFLGLEADFVCCDSLRPFVSQDSLQLSEDEAPYH   800
 801  QATPDHGYLSLLYDSPSGNLSMPHKPVSDKLSEEVDEIWNDLENYIKKNE   850
 851  DKARDRLLAAFPVSKDDVPDRLHAESTPELSRDVGRSVSTLSLPESQALL   900
 901  TPVKSRAGRASRANCPFEEDLISKEGSFMSLNRLSLASEMPLMDNPYDLA   950
 951  NSGLSQTDPENPDLGMEATDKTKSRVFMMARQYSQKIKKANQLLKVKSLE  1000
1001  LEQPPASQHQKSMHKDLAAILEEKKQGGPAIGARIAEYSQLYDQIVFRES  1050
1051  PLKIQKDGWASPQESSLLRSVSPSQVHHGSGDWLLHSTYSNGELADFCLP  1100
1101  PEQDLRSRYPTFEINTKSTPRQLSAACSVPSLQTSDPLPGSVQRCSVVVS  1150
1151  QPNKENWCQDHLYNSLGRKGISAKSQPYHRSQSSSSVLINKSMDSINYPS  1200
1201  DVGKQQLLSLHRSSRCESHQDLLPDIADSHQQGTEKLSDLTLQDSQKVVV  1250
1251  VNRNLPLNAQIATQNYFSNFKETDGDEDDYVEIKSEEDESELELSHNRRR  1300
1301  KSDSKFVDADFSDNVCSGNTLHSLNSPRTPKKPVNSKLGLSPYLTPYNDS  1350
1351  DKLNDYLWRGPSPNQQNIVQSLREKFQCLSSSSFA                 1385

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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