 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Z2V6 from www.uniprot.org...
The NucPred score for your sequence is 0.82 (see score help below)
1 MNSPNESDGMSGREPSLGILPRTPLHSIPVAVEVKPVLPGAMPSSMGGGG 50
51 GGSPSPVELRGALAGPMDPALREQQLQQELLVLKQQQQLQKQLLFAEFQK 100
101 QHDHLTRQHEVQLQKHLKQQQEMLAAKRQQELEQQRQREQQRQEELEKQR 150
151 LEQQLLILRNKEKSKESAIASTEVKLRLQEFLLSKSKEPTPGGLNHSLPQ 200
201 HPKCWGAHHASLDQSSPPQSGPPGTPPSYKLPLLGPYDSRDDFPLRKTAS 250
251 EPNLKVRSRLKQKVAERRSSPLLRRKDGTVISTFKKRAVEITGTGPGVSS 300
301 VCNSAPGSGPSSPNSSHSTIAENGFTGSVPNIPTEMIPQHRALPLDSSPN 350
351 QFSLYTSPSLPNISLGLQATVTVTNSHLTASPKLSTQQEAERQALQSLRQ 400
401 GGTLTGKFMSTSSIPGCLLGVALEGDTSPHGHASLLQHVCSWTGRQQSTL 450
451 IAVPLHGQSPLVTGERVATSMRTVGKLPRHRPLSRTQSSPLPQSPQALQQ 500
501 LVMQQQHQQFLEKQKQQQMQLGKILTKTGELSRQPTTHPEETEEELTEQQ 550
551 EALLGEGALTIPREGSTESESTQEDLEEEEEEEEEEEEDCIQVKDEDGES 600
601 GPDEGPDLEESSAGYKKLFADAQQLQPLQVYQAPLSLATVPHQALGRTQS 650
651 SPAAPGSMKSPTDQPTVVKHLFTTGVVYDTFMLKHQCMCGNTHVHPEHAG 700
701 RIQSIWSRLQETGLLGKCERIRGRKATLDEIQTVHSEYHTLLYGTSPLNR 750
751 QKLDSKKLLGPISQKMYAMLPCGGIGVDSDTVWNEMHSSSAVRMAVGCLV 800
801 ELAFKVAAGELKNGFAIIRPPGHHAEESTAMGFCFFNSVAITAKLLQQKL 850
851 SVGKVLIVDWDIHHGNGTQQAFYNDPSVLYISLHRYDNGNFFPGSGAPEE 900
901 VGGGPGVGYNVNVAWTGGVDPPIGDVEYLTAFRTVVMPIAQEFSPDVVLV 950
951 SAGFDAVEGHLSPLGGYSVTARCFGHLTRQLMTLAGGRVVLALEGGHDLT 1000
1001 AICDASEACVSALLSVELQPLDEAVLQQKPSVNAVATLEKVIEIQSKHWS 1050
1051 CVQRFAAGLGCSLREAQTGEKEEAETVSAMALLSVGAEQAQAVATQEHSP 1100
1101 RPAEEPMEQEPAL 1113
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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