 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6MG49 from www.uniprot.org...
The NucPred score for your sequence is 0.83 (see score help below)
1 MEPSDSTSTAMEEPDSLEVLVKTLDSQTRTFIVGAQMNVKEFKEHIAASV 50
51 SIPSEKQRLIYQGRVLQDDKKLQDYNVGGKVIHLVERAPPQTQLPSGASS 100
101 GTGSASATHGGGPLPGTRGPGASGHDRNANSYVMVGTFNLPSDGSAVDVH 150
151 INMEQAPIQSEPRVRLVMAQHMIRDIQTLLSRMECRGGTQAQASQPPPQT 200
201 PTVASETVALNSQTSEPVESEAPPREPMESEEMEERPPTQTPELPPSGPA 250
251 PAGPAPAPETNAPNHPSPAEHVEVLQELQRLQRRLQPFLQRYCEVLGAAA 300
301 TTDYNNNHEGREEDQRLINLVGESLRLLGNTFVALSDLRCNLACAPPRHL 350
351 HVVRPMSHYTTPMVLQQAAIPIQINVGTTVTMTGNGARPPPAPGAEAASP 400
401 GSGQASSLPPSSATVDSSTEGAPPPGPAPPPATSHPRVIRISHQSVEPVV 450
451 MMHMNIQDSGAQPGGVPSAPTGPLGPPGHGQSLGQQVPGFPTAPTRVVIA 500
501 RPTPPQARPSHPGGPPVSGALQGAGLGTNTSLAQMVSGLVGQLLMQPVLV 550
551 AQGTPGMAPASASAPATAQAQAPAPAPAPAPAPATASASAGTTNTATTAG 600
601 PAPGGPAQPPPPQPSAADLQFSQLLGNLLGPAGPGAGGPSLASPTITVAV 650
651 PGVPAFLQGMTEFLQASQAAPPPPPPPPPPPPAPEQQTTPPPGSPSGGTA 700
701 SPGGLGPESLPPEFFTSVVQGVLSSLLGSLGARAGSSESIAAFIQRLSGS 750
751 SNIFEPGADGALGFFGALLSLLCQNFSMVDVVMLLHGHFQPLQRLQPQLR 800
801 SFFHQHYLGGQEPTSSNIRMATHTLITGLEEYVRESFSLVQVQPGVDIIR 850
851 TNLEFLQEQFNSIAAHVLHCTDSGFGARLLELCNQGLFECLALNLHCLGG 900
901 QQMELAAVINGRIRRMSRGVNPSLVSWLTTMMGLRLQVVLEHMPVGPDAI 950
951 LRYVRRIGDPPQALPEEPMEVQGAERTSPEPQREDASPAPGTTAEEAMSR 1000
1001 GPPPAPEGGSRDEQDGASADAEPWAAAVPPEWVPIIQQDIQSQRKVKPQP 1050
1051 PLSDAYLSGMPAKRRKTMQGEGPQLLLSEAVSRAAKAAGARPLTSPESLS 1100
1101 RDLEAPEVQESYRQQLRSDIQKRLQEDPNYSPQRFPNAHRAFADDP 1146
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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