 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O08962 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCE 50
51 LCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYR 100
101 KDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGP 150
151 STSWLASGRAKTFRLKLPALLALTARESPMRTGSTGSPGAPGAVVVDVDL 200
201 TPAAPSSESLALDEVSAMDNHVAGLGPAEERRALVGPASASPVASIPGPH 250
251 PSPRAQSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGALPL 300
301 PPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPF 350
351 LASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRW 400
401 TILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEDGSQAPDCGYA 450
451 CQPLAVVDLLVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWF 500
501 LIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAA 550
551 VLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSHIGWLHNLGDQIGKPYN 600
601 SSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIG 650
651 SLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLE 700
701 EYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATK 750
751 GCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAI 800
801 LGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEF 850
851 SDHFWSSLEITFNLRDTNMIPGSPSSAELESGFNRQRKRKLSFRRRTDKD 900
901 TEQPGEVSALGQGPARVGPGPSCRGQPGGPWGESPSSGPSSPESSEDEGP 950
951 GRSSSPLRLVPFSSPRPPGDSPGGEPLTEDGEKSSDTCNPLSGAFSGVSN 1000
1001 IFSFWGDSRGRQYQELPRCPAPAPSLLNIPLSSPGRRSRGDVESRLDALQ 1050
1051 RQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLP 1100
1101 VGPVPTLTLDSLSQVSQFVAFEELPAGAPELPQDGPTRRLSLPGQLGALT 1150
1151 SQPLHRHGSDPGS 1163
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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