 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q95UN8 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MLPISEEQQLQQQQQQQQLEQLHHPQIPEIPIPDLEQVETQVGDGSLWTA 50
51 LYDYDAQGEDELTLRRGEIVVVLSTDSEVSGDVGWWTGKIGDKVGVFPKD 100
101 FVTDEDPLQLNVSSAIGDIQPHEIEYNELDIKEVIGSGGFCKVHRGYYDG 150
151 EEVAIKIAHQTGEDDMQRMRDNVLQEAKLFWALKHENIAALRGVCLNTKL 200
201 CLVMEYARGGSLNRILAGKIPPDVLVNWAIQIARGMNYLHNEAPMSIIHR 250
251 DLKSSNVLIYEAIEGNHLQQKTLKITDFGLAREMYNTQRMSAAGTYAWMP 300
301 PEVISVSTYSKFSDVWSYGVLLWELITGETPYKGFDPLSVAYGVAVNTLT 350
351 LPIPKTCPETWGALMKSCWQTDPHKRPGFKEILKQLESIACSKFTLTPQE 400
401 SFHYMQECWRKEIAGVLHDLREKEKELRNKEEQLLRVQNEQREKANLLKI 450
451 REQNLRERERVLIERELVMLQPVPSKRKHKKGKKNKPLQISLPTGFRHTI 500
501 TAVRDKAEQPGSPSFSGLRIVALTDGHKGKTWGPSTMHQRERSLLPSQLS 550
551 GGQPEWPAQTSTHSSFSKSAPNLDKKQQQQNQQQVASLTPPPGLGILGGS 600
601 GGAGGTPATPLLYPGIPIILTRPNNNNIGNCKAITTTITTTTTTTTNNNN 650
651 NNNNSISANNNNQLNNISTINSNNNNNQTNLTSQPNTIIVLQNGRNNSNS 700
701 STTSQSPAKIYHRARSQEYGLDHPLAYQPPPLYLVTDDSSETDTVASPTG 750
751 CFHFLKSGNSSAASGAVHLHRFGGSLGNSPAVGRKKHSLDSSSHHPPANG 800
801 SNSFALPNQLTLPSEDNNTYDHAFYRDVIKKMSMASSERVNSKSSGDLTM 850
851 YNSSTPLTARDCDDAEEAFEGGRFQRNFSGSQFPRHCFFTRQEEEGEAED 900
901 EDAVAAEVDTADADADDECQVPASQMRQNSTTSRKSSVTFQSVSFEEPDF 950
951 VATPRTTARSDLYTSSASISFATYRSASPSLSSSSTTASASPSIASTEAV 1000
1001 NGYHMQENSILNTRRMQDVQPHPDVIKLRAQEQRQQTKNQKKQRPKHITK 1050
1051 SKSVEAPVEGQHHEHDDHNDPQHQHHSAGSSKIRALFNLFTRSRKKYSKL 1100
1101 AEHNMVGGPEFCAIDPYQTDLAMGGSSRSLKRKGKKPQTQSCEQLERC 1148
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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