 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Z0Z4 from www.uniprot.org...
The NucPred score for your sequence is 0.11 (see score help below)
1 MKAGHLLWALLLMHSLWSIPTDGAIRNYYLGIQDMQWNYAPKGRNVITNQ 50
51 TLNNDTVASSFLKSGKNRIGSSYKKTVYKEYSDGTYTEEIAKPAWLGFLG 100
101 PLLQAEVGDVILIHLKNFASRPYTIHPHGVFYEKDSEGSLYPDGSSGYLK 150
151 ADDSVPPGGSHVYNWSIPESHAPTEADPACLTWIYHSHVDAPRDIATGLI 200
201 GPLITCKRGTLDGNSPPQRKDVDHNFFLLFSVIDENLSWHLDDNIATYCS 250
251 DPASVDKEDGAFQDSNRMHAINGFVFGNLPELSMCAQKHVAWHLFGMGNE 300
301 IDVHTAFFHGQMLSIRGHHTDVANIFPATFVTAEMVPQKSGTWLISCEVN 350
351 SHLRSGMQAFYKVDSCSMDPPVDQLTGKVRQYFIQAHEIQWDYGPIGYDG 400
401 RTGKSLREPGSGPDKYFQKSSSRIGGTYWKVRYEAFQDETFQERVHQEEE 450
451 THLGILGPVIRAEVGDTIQVVFYNRASQPFSIQPHGVFYEKNSEGTVYND 500
501 GTSHPKVAKSFEKVTYYWTVPPHAGPTAQDPACLTWMYFSAADPTRDTNS 550
551 GLVGPLLVCKAGALGADGKQKGVDKEFFLLFTVFDENESWYNNANQAAGM 600
601 LDSRLLSEDVEGFQDSNRMHAINGFLFSNLPRLDMCKGDTVAWHLLGLGT 650
651 ETDVHGVMFEGNTVQLQGMRKGAVMLFPHTFVTAIMQPDNPGIFEIYCQA 700
701 GSHREEGMQAIYNVSQCSSHQDSPRQHYQASRVYYIMAEEIEWDYCPDRS 750
751 WELEWHNTSEKDSYGHVFLSNKDGLLGSKYKKAVFREYTDGTFRIPRPRS 800
801 GPEEHLGILGPLIRGEVGDILTVVFKNKASRPYSIHAHGVLESNTGGPQA 850
851 AEPGEVLTYQWNIPERSGPGPSDSACVSWIYYSAVDPIKDMYSGLVGPLV 900
901 ICRNGILEPNGGRNDMDREFALLFLIFDENQSWYLKENIATYGPQESSHV 950
951 NLKDATFLESNKMHAINGKLYANLRGLTVYQGERVAWYMLAMGQDTDIHT 1000
1001 VHFHAESFLYQNGQSYRADVVDLFPGTFEVVEMVASNPGTWLMHCHVTDH 1050
1051 VHAGMETIFTVLSHEEHFSTMTTITKEIGKAVILRDIGGDNVKMLGMNIP 1100
1101 IKDVEILSSALIAICVLLLLIALALGGVVWYQHRQRKLRRNRRSILDDSF 1150
1151 KLLSLKQ 1157
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.) |
Go back to the NucPred Home Page.