 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Y2G3 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MWRWIRQQLGFDPPHQSDTRTIYVANRFPQNGLYTPQKFIDNRIISSKYT 50
51 VWNFVPKNLFEQFRRVANFYFLIIFLVQLMIDTPTSPVTSGLPLFFVITV 100
101 TAIKQGYEDWLRHNSDNEVNGAPVYVVRSGGLVKTRSKNIRVGDIVRIAK 150
151 DEIFPADLVLLSSDRLDGSCHVTTASLDGETNLKTHVAVPETALLQTVAN 200
201 LDTLVAVIECQQPEADLYRFMGRMIITQQMEEIVRPLGPESLLLRGARLK 250
251 NTKEIFGVAVYTGMETKMALNYKSKSQKRSAVEKSMNTFLIIYLVILISE 300
301 AVISTILKYTWQAEEKWDEPWYNQKTEHQRNSSKILRFISDFLAFLVLYN 350
351 FIIPISLYVTVEMQKFLGSFFIGWDLDLYHEESDQKAQVNTSDLNEELGQ 400
401 VEYVFTDKTGTLTENEMQFRECSINGMKYQEINGRLVPEGPTPDSSEGNL 450
451 SYLSSLSHLNNLSHLTTSSSFRTSPENETELIKEHDLFFKAVSLCHTVQI 500
501 SNVQTDCTGDGPWQSNLAPSQLEYYASSPDEKALVEAAARIGIVFIGNSE 550
551 ETMEVKTLGKLERYKLLHILEFDSDRRRMSVIVQAPSGEKLLFAKGAESS 600
601 ILPKCIGGEIEKTRIHVDEFALKGLRTLCIAYRKFTSKEYEEIDKRIFEA 650
651 RTALQQREEKLAAVFQFIEKDLILLGATAVEDRLQDKVRETIEALRMAGI 700
701 KVWVLTGDKHETAVSVSLSCGHFHRTMNILELINQKSDSECAEQLRQLAR 750
751 RITEDHVIQHGLVVDGTSLSLALREHEKLFMEVCRNCSAVLCCRMAPLQK 800
801 AKVIRLIKISPEKPITLAVGDGANDVSMIQEAHVGIGIMGKEGRQAARNS 850
851 DYAIARFKFLSKLLFVHGHFYYIRIATLVQYFFYKNVCFITPQFLYQFYC 900
901 LFSQQTLYDSVYLTLYNICFTSLPILIYSLLEQHVDPHVLQNKPTLYRDI 950
951 SKNRLLSIKTFLYWTILGFSHAFIFFFGSYLLIGKDTSLLGNGQMFGNWT 1000
1001 FGTLVFTVMVITVTVKMALETHFWTWINHLVTWGSIIFYFVFSLFYGGIL 1050
1051 WPFLGSQNMYFVFIQLLSSGSAWFAIILMVVTCLFLDIIKKVFDRHLHPT 1100
1101 STEKAQLTETNAGIKCLDSMCCFPEGEAACASVGRMLERVIGRCSPTHIS 1150
1151 RSWSASDPFYTNDRSILTLSTMDSSTC 1177
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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