 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q04893 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MGSSGSKSTTATTTSHSSTTTTSSTTSTTTPTTTSTTSTTSTKVTTSPEI 50
51 IVSSSSTLVSSVVPEFTSSSSLSSDTIASILSSESLVSIFSSLSYTSSDI 100
101 SSTSVNDVESSTSGPSNSYSALSSTNAQLSSSTTETDSISSSAIQTSSPQ 150
151 TSSSNGGGSSSEPLGKSSVLETTASSSDTTAVTSSTFTTLTDVSSSPKIS 200
201 SSGSAVTSVGTTSDASKEVFSSSTSDVSSLLSSTSSPASSTISETLPFSS 250
251 TILSITSSPVSSEAPSATSSVISSEASWATSSSVSSEAPLATSSVVSSEA 300
301 PSSTSSVVSSEAPSSTSSSVSSEISSTTSSSVSSEAPLATSSVVSSEAPS 350
351 STSSSVSSEISSTTSSSVSSEAPLATSSVVSSEAPSSTSSSVSSEAPSST 400
401 SSSVSSEAPSSTSSSVSSEISSTKSSVMSSEVSSATSSLVSSEAPSAISS 450
451 LASSRLFSSKNTSVTSTLVATEASSVTSSLRPSSETLASNSIIESSLSTG 500
501 YNSTVSTTTSAASSTLGSKVSSSNSRMATSKTSSTSSDLSKSSVIFGNSS 550
551 TVTTSPSASISLTASPLPSVWSDITSSEASSISSNLASSSAPSDNNSTIA 600
601 SASLIVTKTKNSVVSSIVSSITSSETTNESNLATSSTSLLSNKATARSLS 650
651 TSNATSASNVPTGTFSSMSSHTSVITPGFSTSSASLAINSTVVSSSLAGY 700
701 SFSTPESSPTTSTLVTSEAPSTVSSMTTSAPFINNSTSARPSPSTASFIT 750
751 ESTSSISSVPLASGDVTSSLAAHNLTTFSAPSTSSAQLVSKSTTSSSILV 800
801 TPRIDRSGNSSTASRIATSLPNKTTFVSSLSSTSAHARNIFNSTVLATAK 850
851 QIETLTSTVNCSNPTPNYNITKTVIVSRETTAIGTVTSCSGGCTKNRKST 900
901 TLITITDIDASTVTTCPEKEVTSTTSGDEAEHTTSTKISNFETSTFSESF 950
951 KDMKTSQETKKAKPGSETVRSSSSFVEKTSPTTKASPSTSPSESKAAGNT 1000
1001 SVATNASPSTSPSESQGTGSTSVEGAKSKSTKNSEGVSTTKAKNTSTVAK 1050
1051 SSTESPIGRGETTLETIIVSSQKSLLTSQLSSSTEKVNRSTTKPTAAIHG 1100
1101 TSSSAKQSTTYTVSTAKENTGASLNINMKAFVIGAIALVA 1140
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
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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