 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Z1R2 from www.uniprot.org...
The NucPred score for your sequence is 0.81 (see score help below)
1 MEPSDSASTAMEEPDSLEVLVKTLDSQTRTFIVGAQMNVKEFKEHIAASV 50
51 SIPSEKQRLIYQGRVLQDDKKLQEYNVGGKVIHLVERAPPQTQLPSGASS 100
101 GTGSASATHGGAPLPGTRGPGASVHDRNANSYVMVGTFNLPSDGSAVDVH 150
151 INMEQAPIQSEPRVRLVMAQHMIRDIQTLLSRMECRGGTQAQASQPPPQT 200
201 PQTVASETVALNSQTSEPVESEAPPREPMESEEMEERPPTQTPELAPSGP 250
251 APAGPAPAGPAPAPETNAPNHPSPAEHVEVLQELQRLQRRLQPFLQRYCE 300
301 VLGAAATTDYNNNHEGREEDQRLINLVGESLRLLGNTFVALSDLRCNLAC 350
351 APPRHLHVVRPMSHYTTPMVLQQAAIPIQINVGTTVTMTGNGARPPPAPG 400
401 AEAATPGSAQATSLPPSSTTVDSSTEGAPPPGPAPPPASSHPRVIRISHQ 450
451 SVEPVVMMHMNIQDSGAQPGGVPSAPTGPLGPPGHGQTLGQQVPGFPTAP 500
501 TRVVIARPTPPQARPSHPGGPPVSGALQGAGLGTNTSLAQMVSGLVGQLL 550
551 MQPVLVAQGTPGMAQAQAQAQAQAQAQAQAPAPAPAPAPAPATASASAGT 600
601 TNTATTAGPAPGGPAQPPPPQPSAADLQFSQLLGNLLGPAGPGAGGPGMA 650
651 SPTITVAMPGVPAFLQGMTDFLQASQTAPPPPPPPPPPPPAPEQQSTPPP 700
701 GSPSGGTASPGGLGPESLPPEFFTSVVQGVLSSLLGSLGARAGSSESIAA 750
751 FIQRLSGSSNIFEPGADGALGFFGALLSLLCQNFSMVDVVMLLHGHFQPL 800
801 QRLQPQLRSFFHQHYLGGQEPTPSNIRMATHTLITGLEEYVRESFSLVQV 850
851 QPGVDIIRTNLEFLQEQFNSIAAHVLHCTDSGFGARLLELCNQGLFECLA 900
901 LNLHCLGGQQMELAAVINGRIRRMSRGVNPSLVSWLTTMMGLRLQVVLEH 950
951 MPVGPDAILRYVRRVGDPPQTLPEEPMEVQGAERTSPEPQRENASPAPGT 1000
1001 TAEEAMSRGPPPAPEGGSRDEQDGASADAEPWAAAVPPEWVPIIQQDIQS 1050
1051 QRKVKPQPPLSDAYLSGMPAKRRKTMQGEGPQLLLSEAVSRAAKAAGARP 1100
1101 LTSPESLSRDLEAPEVQESYRQQLRSDIQKRLQEDPNYSPQRFPNAHRAF 1150
1151 ADDP 1154
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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