 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P47108 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 MGDLTEELSIPDNAQDLSKLLRSTSTKPHQIAEIVSKFDKLETYFPKKEI 50
51 FVLDLLIDRLNNGNLDDFKTSEHTWIIFTRLLDAINDPISIKKLLKKLKT 100
101 VPVMIRTFFLWPKDKLLTRSVSFIKAFFAINDYLIVNFSVEESFQLLEHA 150
151 INGLSSCPTTDFALSYLQDACNLTHVDNITTTDNKIATCYCKHMLLPSLR 200
201 YFAQTKNSASSNQSFIRLSHFMGKFLLQPRIDYMKLNKKFVQENASEITD 250
251 DMAYYYFATFVTFLSKDNFAQLEVIFTILGAKKPSLECRFLNLLSESKKT 300
301 VSQEFLEALLLEMLASTDESGVLSLIPIILKLDIEVAIKHIFRLLELIQL 350
351 ENLNDPLFSSHIWDLIIQSHANARELSDFFAKINEYCSRKGPDSYFLINH 400
401 PAYVKSITKQLFTLSSLQWKNLLQALLDQVNHDSTNRVPLYLIRICLEGL 450
451 SEGASRATLDEVKPILSQVFTLESFNNSLQWDLKYHIMEVYDDIVPAEEL 500
501 EKIDYVLSSNIFDTTSADVEELFFYCFKLREYISFDLSDAKKKFMRHFEI 550
551 LDEERKSNLSYSVVSKFATLVNNNFTREQISSLIDSLLLNSTNLSSLLKN 600
601 DDIFEETNITYALINKLASSYHQTFALEALIQIPIQCINKNVRVALINNL 650
651 TCESFCLDSATRECLLHLLSSPTFKSNIETNFYELCEKTIMSPEMAISET 700
701 GDEKKEIEDKISIFEKVWTNHLSQAKEPVSEKFLESGYDIVKQSMSLSNG 750
751 DSKLIIAGFTIAKFLKPDNKHRDIQGMAISYAVKILENYSENFESETIPL 800
801 FRISMSTLYKIITTGQGDISKHKSRILDIFSKIMLRYHSKKVYHAPEEQE 850
851 MFLVHSLLTENKLEYIFAEYLNIEHTDKCDSALGFCLEESLKQGPDAFNR 900
901 LLWNSAKSFSTISQPCAEKFVRVFIIMSKRIARDNNLGHHLFVIALLEAY 950
951 TYCDIEKFGYKSYLLLFNAIKEFLVSKPWLFSQYCIEMLLPFCLKTLAFI 1000
1001 VNHESTDEINEGFINIIEVIDHMLLVHRFKFSNRHHLFNSVLCQILEIIA 1050
1051 IHDGTLCANSADAVARLITNYCEPYNVSNAQNGQKNNLSSKISLIKQSIR 1100
1101 KNVLVVLTKYIQLSITTQFSLNIKKSLQPGIHAIFDILSQNELNQLNAFL 1150
1151 DTPGKQYFKALYLQYKKVGKWRED 1174
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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