 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q2VLG4 from www.uniprot.org...
The NucPred score for your sequence is 0.39 (see score help below)
1 MSKLRMVLLEDSGSADVRRHFVNLSPFTIAVVLLLRACFVTSSLGGTTKE 50
51 LRLVDGENKCSGRVEVKIQEEWGTVCNNGWSMEAVSVICNQLGCPTAIKA 100
101 TGWANSSAGSGRIWMDHVSCRGNESALWDCKHDGWGKHSNCTHQQDAGVT 150
151 CSDGSDLEMRLTNGGNMCSGRIEIKFQGQWGTVCDDNFNINHASVVCKQL 200
201 ECGSAVSFSGSANFGEGSGPIWFDDLICNGNESALWNCKHQGWGKHNCDH 250
251 AEDAGVICSKGADLSLRLVDGVTECSGRLEVRFQGEWGTICDDGWDSHDA 300
301 AVACKQLGCPTAITAIGRVNASEGFGHIWLDSVSCQGHEPAVWQCKHHEW 350
351 GKHYCNHNEDAGVTCSDGSDLELRLRGGGSRCAGTVEVEIQRLLGKVCDR 400
401 GWGLKEADVVCRQLGCGSALKTSYQVYSKIQATNMWLFLSSCNGNETSLW 450
451 DCKNWQWGGLTCDHYEEAKITCSAHREPRLVGGDIPCSGRVEVKHGDTWG 500
501 SVCDSDFSLEAASVLCRELQCGTVVSILGGAHFGEGNGQIWTEEFQCEGH 550
551 ESHLSLCPVAPRPEGTCSHSRDVGVVCSRYTEIRLVNGKTPCEGRVELKT 600
601 LNAWGSLCNSHWDIEDAHVLCQQLKCGVALSTPGGAHFGKGNGQVWRHMF 650
651 HCTGTEQHMGDCPVTALGASLCPSGQVASVICSGNQSQTLSSCNSSSLGP 700
701 TRPTIPEESAVACIESGQLRLVNGGGRCAGRVEIYHEGSWGTICDDSWDL 750
751 SDAHVVCRQLGCGEAINATGSAHFGEGTGPIWLDEMKCNGKESRIWQCHS 800
801 HGWGQQNCRHKEDAGVICSEFMSLRLTSEASREACAGRLEVFYNGAWGSV 850
851 GRSNMSETTVGVVCRQLGCADKGKINSASLDKAMSIPMWVDNVQCPKGPD 900
901 TLWQCPSSPWEKRLARPSEETWITCDNKMRLQEGPTSCSGRVEIWHGGSW 950
951 GTVCDDSWDLNDAQVVCQQLGCGPALKAFKEAEFGQGTGPIWLNEVKCKG 1000
1001 NESSLWDCPARRWGHSECGHKEDAAVNCTDISTRKTPQKATTGQSSLIAV 1050
1051 GILGVVLLAIFVALFLTQKRRQRQRLTVSSRGENLVHQIQYREMNSCLNA 1100
1101 DDLDLMNSSENSNESADFNAAELISVSKFLPISGMEKEAILRHTEKENGN 1150
1151 L 1151
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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