 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q4R6B2 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MCGPAMFPAGPRWPRVRVLQVLWALLAVLLASRRLWAIKDFEECTWQVVL 50
51 NEFKRVGENGASDRFFEQELVDTVGNLFHMLVDSPIDPREKYLGFPYYLK 100
101 INYSCEEKHSEDLVRMGHLTGLKPVVLVTFQSPVNFYRWKIEQLQIQMEA 150
151 APFRSKEPCIAEEVCSMSWYTPMPIKNGSVVTRVDVSSNGLGTFIPDKRF 200
201 QVNINGFLKRNQDNDIQFTVGDELFNLMPQYFVGISSRPLWHTVDQSPVL 250
251 ILGGIPNEKYVLMTDTSFKDFSLVELSIDSCWVGSFYCPQSGFTATIYDT 300
301 VATESTLFIRQNQLVYYFTGTYTTLYERNRGSGSWVRVLASECIKKLCPV 350
351 YFHSNGSEYIMALTTGKHEGFVHFGTIRDGQVSFEMLPREWSVCEQIGVT 400
401 TCSIIWSDYIAGEYTLLLLVESEYENASKRFQVVSYNTANDDLELLYHIP 450
451 EFIPEARGLEFLMILGTESYTNTVMTPKGISCNPYNHLIFIWGNFLLQSS 500
501 NKENFIYLADFPKELSIKYMTRSFRGAVAIVTETEEIWYLLEGTYRVYRL 550
551 FPSKGWKVHISLQLMQQSSLYASNETMLTLFYEGSKLYQLVYLMNNQKGQ 600
601 LVKRLMPVEQLLMYQQHTSHYDLDRKGGYLMLSFTNFCPFSVMRLRNLPG 650
651 PQRYTRQERYRARPPHVLERSGFHNENSLAIYQGLIYYLLWLHSVYDKPY 700
701 ADPVHDPTWRWWENNKQDQDYYFFLASNWRSAGGVFIEMDSYEKIYNLKS 750
751 AYELPERIFLDKGTEYSFAIFLSAQSRSFRTMADLGTVFELHSHVDVGVV 800
801 LADPGCIEASVKQEVLINRNAVLFSITLKDKKVCYDQGISGHHLMKSSMT 850
851 VNVVGSSGLCFQETHAGARMQGNLMVPVFIGCPPGKRLAFDITYTLEYSR 900
901 LKNKHYFDCVQVDPEMPCFLFRDIFYPFFLIQDLVTGDSGSFQGSYVLLV 950
951 VGGGPTLDTLKDYNKDEIYRFNSPLDKTHSLIWTTRTKRTTKDSAFHIMS 1000
1001 HESPGIEWLCLENAPCYDNVPQGIFAPEFFFKVLVSNRGVDTSTYCNYQL 1050
1051 TFLLHIHGLPLSPKRALFILMVSLSVFVGLVIFYIAFCLLWPLVVKGCTM 1100
1101 IRWKINDIIASESYYTYASISGMSSMQSLRRSRMGSMFSSRMTEDKAEPK 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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