 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6ZRH7 from www.uniprot.org...
The NucPred score for your sequence is 0.37 (see score help below)
1 MCGPAMFPAGPPWPRVRVVQVLWALLAVLLASWRLWAIKDFQECTWQVVL 50
51 NEFKRVGESGVSDSFFEQEPVDTVSSLFHMLVDSPIDPSEKYLGFPYYLK 100
101 INYSCEEKPSEDLVRMGHLTGLKPLVLVTFQSPVNFYRWKIEQLQIQMEA 150
151 APFRSKEPCMAEEVCSMSWYTPMPIKKGSVVMRVDISSNGLGTFIPDKRF 200
201 QMNINGFLKRDRDNNIQFTVGEELFNLMPQYFVGVSSRPLWHTVDQSPVL 250
251 ILGGIPNEKYVLMTDTSFKDFSLVELSIDSCWVGSFYCPHSGFTATIYDT 300
301 IATESTLFIRQNQLVYYFTGTYTTLYERNRGSGSWIRVLASECIKKLCPV 350
351 YFHSNGSEYIMALTTGKHEGYVHFGTIRDGQVSFEMLPRQWSVCEQIGVT 400
401 TCSIIWSEYIAGEYTLLLLVESGYGNASKRFQVVSYNTASDDLELLYHIP 450
451 EFIPEARGLEFLMILGTESYTSTAMAPKGIFCNPYNNLIFIWGNFLLQSS 500
501 NKENFIYLADFPKELSIKYMARSFRGAVAIVTETEEIWYLLEGSYRVYQL 550
551 FPSKGWQVHISLKLMQQSSLYASNETMLTLFYEDSKLYQLVYLMNNQKGQ 600
601 LVKRLVPVEQLLMYQQHTSHYDLERKGGYLMLSFIDFCPFSVMRLRSLPS 650
651 PQRYTRQERYRARPPRVLERSGFHNENSLAIYQGLVYYLLWLHSVYDKPY 700
701 ADPVHDPTWRWWANNKQDQDYYFFLASNWRSAGGVSIEMDSYEKIYNLES 750
751 AYELPERIFLDKGTEYSFAIFLSAQGHSFRTQSELGTAFQLHSQVDVGVV 800
801 LADPGCIEASVKQEVLINRNSVLFSITLKDKKLCYDQGISGHHLMETSMT 850
851 VNVVGSSGLCFQETHLGPHMQGNLMVPVFIGCPPGKRLAFDITYTLEYSR 900
901 LKNKHYFDCVNVNPEMPCFLFRDIFYPFFLIQDLVTGDSGSFQGSYVLLV 950
951 VGGGPTLDSLKDYSEDEIYRFNSPLDKTNSLIWTTRTTRTTKDSAFHIMS 1000
1001 HESPGIEWLCLENAPCYDNVPQGIFAPEFFFKVLVSNRGVDTSTYCNYQL 1050
1051 TFLLHIHGLPLSPKRALFIIMVSASVFVGLVIFYIAFCLLWPLVVKGCTM 1100
1101 IRWKINNLIASESYYTYASISGISSMPSLRHSRMGSMFSSRMTEDRAEPK 1150
1151 EAVERQLMT 1159
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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