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

NucPred

Fetching Q92752 from www.uniprot.org...

The NucPred score for your sequence is 0.35 (see score help below)

   1  MGADGETVVLKNMLIGINLILLGSMIKPSECQLEVTTERVQRQSVEEEGG    50
  51  IANYNTSSKEQPVVFNHVYNINVPLDNLCSSGLEASAEQEVSAEDETLAE   100
 101  YMGQTSDHESQVTFTHRINFPKKACPCASSAQVLQELLSRIEMLEREVSV   150
 151  LRDQCNANCCQESAATGQLDYIPHCSGHGNFSFESCGCICNEGWFGKNCS   200
 201  EPYCPLGCSSRGVCVDGQCICDSEYSGDDCSELRCPTDCSSRGLCVDGEC   250
 251  VCEEPYTGEDCRELRCPGDCSGKGRCANGTCLCEEGYVGEDCGQRQCLNA   300
 301  CSGRGQCEEGLCVCEEGYQGPDCSAVAPPEDLRVAGISDRSIELEWDGPM   350
 351  AVTEYVISYQPTALGGLQLQQRVPGDWSGVTITELEPGLTYNISVYAVIS   400
 401  NILSLPITAKVATHLSTPQGLQFKTITETTVEVQWEPFSFSFDGWEISFI   450
 451  PKNNEGGVIAQVPSDVTSFNQTGLKPGEEYIVNVVALKEQARSPPTSASV   500
 501  STVIDGPTQILVRDVSDTVAFVEWIPPRAKVDFILLKYGLVGGEGGRTTF   550
 551  RLQPPLSQYSVQALRPGSRYEVSVSAVRGTNESDSATTQFTTEIDAPKNL   600
 601  RVGSRTATSLDLEWDNSEAEVQEYKVVYSTLAGEQYHEVLVPRGIGPTTR   650
 651  ATLTDLVPGTEYGVGISAVMNSQQSVPATMNARTELDSPRDLMVTASSET   700
 701  SISLIWTKASGPIDHYRITFTPSSGIASEVTVPKDRTSYTLTDLEPGAEY   750
 751  IISVTAERGRQQSLESTVDAFTGFRPISHLHFSHVTSSSVNITWSDPSPP   800
 801  ADRLILNYSPRDEEEEMMEVSLDATKRHAVLMGLQPATEYIVNLVAVHGT   850
 851  VTSEPIVGSITTGIDPPKDITISNVTKDSVMVSWSPPVASFDYYRVSYRP   900
 901  TQVGRLDSSVVPNTVTEFTITRLNPATEYEISLNSVRGREESERICTLVH   950
 951  TAMDNPVDLIATNITPTEALLQWKAPVGEVENYVIVLTHFAVAGETILVD  1000
1001  GVSEEFRLVDLLPSTHYTATMYATNGPLTSGTISTNFSTLLDPPANLTAS  1050
1051  EVTRQSALISWQPPRAEIENYVLTYKSTDGSRKELIVDAEDTWIRLEGLL  1100
1101  ENTDYTVLLQAAQDTTWSSITSTAFTTGGRVFPHPQDCAQHLMNGDTLSG  1150
1151  VYPIFLNGELSQKLQVYCDMTTDGGGWIVFQRRQNGQTDFFRKWADYRVG  1200
1201  FGNVEDEFWLGLDNIHRITSQGRYELRVDMRDGQEAAFASYDRFSVEDSR  1250
1251  NLYKLRIGSYNGTAGDSLSYHQGRPFSTEDRDNDVAVTNCAMSYKGAWWY  1300
1301  KNCHRTNLNGKYGESRHSQGINWYHWKGHEFSIPFVEMKMRPYNHRLMAG  1350
1351  RKRQSLQF                                            1358

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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