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

NucPred

Fetching O15047 from www.uniprot.org...

The NucPred score for your sequence is 1.00 (see score help below)

   1  MDQEGGGDGQKAPSFQWRNYKLIVDPALDPALRRPSQKVYRYDGVHFSVN    50
51 DSKYIPVEDLQDPRCHVRSKNRDFSLPVPKFKLDEFYIGQIPLKEVTFAR 100
101 LNDNVRETFLKDMCRKYGEVEEVEILLHPRTRKHLGLARVLFTSTRGAKE 150
151 TVKNLHLTSVMGNIIHAQLDIKGQQRMKYYELIVNGSYTPQTVPTGGKAL 200
201 SEKFQGSGAATETAESRRRSSSDTAAYPAGTTAVGTPGNGTPCSQDTSFS 250
251 SSRQDTPSSFGQFTPQSSQGTPYTSRGSTPYSQDSAYSSSTTSTSFKPRR 300
301 SENSYQDAFSRRHFSASSASTTASTAIAATTAATASSSASSSSLSSSSSS 350
351 SSSSSSSQFRSSDANYPAYYESWNRYQRHTSYPPRRATREEPPGAPFAEN 400
401 TAERFPPSYTSYLPPEPSRPTDQDYRPPASEAPPPEPPEPGGGGGGGGPS 450
451 PEREEVRTSPRPASPARSGSPAPETTNESVPFAQHSSLDSRIEMLLKEQR 500
501 SKFSFLASDTEEEEENSSMVLGARDTGSEVPSGSGHGPCTPPPAPANFED 550
551 VAPTGSGEPGATRESPKANGQNQASPCSSGDDMEISDDDRGGSPPPAPTP 600
601 PQQPPPPPPPPPPPPPYLASLPLGYPPHQPAYLLPPRPDGPPPPEYPPPP 650
651 PPPPHIYDFVNSLELMDRLGAQWGGMPMSFQMQTQMLTRLHQLRQGKGLI 700
701 AASAGPPGGAFGEAFLPFPPPQEAAYGLPYALYAQGQEGRGAYSREAYHL 750
751 PMPMAAEPLPSSSVSGEEARLPPREEAELAEGKTLPTAGTVGRVLAMLVQ 800
801 EMKSIMQRDLNRKMVENVAFGAFDQWWESKEEKAKPFQNAAKQQAKEEDK 850
851 EKTKLKEPGLLSLVDWAKSGGTTGIEAFAFGSGLRGALRLPSFKVKRKEP 900
901 SEISEASEEKRPRPSTPAEEDEDDPEQEKEAGEPGRPGTKPPKRDEERGK 950
951 TQGKHRKSFALDSEGEEASQESSSEKDEEDDEEDEEDEDREEAVDTTKKE 1000
1001 TEVSDGEDEESDSSSKCSLYADSDGENDSTSDSESSSSSSSSSSSSSSSS 1050
1051 SSSSSSSSESSSEDEEEEERPAALPSASPPPREVPVPTPAPVEVPVPERV 1100
1101 AGSPVTPLPEQEASPARPAGPTEESPPSAPLRPPEPPAGPPAPAPRPDER 1150
1151 PSSPIPLLPPPKKRRKTVSFSAIEVVPAPEPPPATPPQAKFPGPASRKAP 1200
1201 RGVERTIRNLPLDHASLVKSWPEEVSRGGRSRAGGRGRLTEEEEAEPGTE 1250
1251 VDLAVLADLALTPARRGLPALPAVEDSEATETSDEAERPRPLLSHILLEH 1300
1301 NYALAVKPTPPAPALRPPEPVPAPAALFSSPADEVLEAPEVVVAEAEEPK 1350
1351 PQQLQQQREEGEEEGEEEGEEEEEESSDSSSSSDGEGALRRRSLRSHARR 1400
1401 RRPPPPPPPPPPRAYEPRSEFEQMTILYDIWNSGLDSEDMSYLRLTYERL 1450
1451 LQQTSGADWLNDTHWVHHTITNLTTPKRKRRPQDGPREHQTGSARSEGYY 1500
1501 PISKKEKDKYLDVCPVSARQLEGVDTQGTNRVLSERRSEQRRLLSAIGTS 1550
1551 AIMDSDLLKLNQLKFRKKKLRFGRSRIHEWGLFAMEPIAADEMVIEYVGQ 1600
1601 NIRQMVADMREKRYVQEGIGSSYLFRVDHDTIIDATKCGNLARFINHCCT 1650
1651 PNCYAKVITIESQKKIVIYSKQPIGVDEEITYDYKFPLEDNKIPCLCGTE 1700
1701 SCRGSLN 1707

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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