 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q2KN97 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MKKASRSVGSVPKVPGVNKTQTTEKTKPESGSSLSAVTKLSKPGTSASLL 50
51 KTKSNDDLLAGMASGGGVTMTNGVKAKKSTCASTVSSTTGTTMSTLENKP 100
101 RTVAGSTARRSTSSGTKESSSSRERIRDRSRLSQSKKLPLAGQGSNDTVL 150
151 AKRSRSRTNPESDIRMSKSKSDNQISDKAALEAKVNDLLTLAKTKDVEIL 200
201 HLRNELRDMRAQLGLNEDQVEGDEKSEEKEAIVVHQPTDVESTLLQLQEQ 250
251 NTAIREELNQLKNENRMLKDRLNALGFSLEQRLDNSEKLFGYQSLSPEIT 300
301 AGNHSDGGGTLTSSVEGSAPGSMEDLLSQDEHTLMDNQHSNSMDNLDSEC 350
351 SEVYQPLTSSDDALDAPSSSESEGVPSIERSRKGSSGNASEVSVACLTER 400
401 IHQMEENQHSTAEELQATLQELADLQQITQELNSENERLGEEKVILMESL 450
451 CQQSDKLEHFSRQIEYFRSLLDEHHISYVIDEDMKSGRYMELEQRYMDLA 500
501 ENARFEREQLLGVQQHLSNTLKMAEQDNKEAQEMIGALKERNHHMERIIE 550
551 SEQKSKTAIASTLEEYKATVASDQIEMNRLKAQLEHEKQKVAELYSIHNS 600
601 GDKSDIQDLLESVRLDKEKAETLASSLQEELAHTRNDANRLQDAIAKVED 650
651 EYRVFQEEAKKQIEDLNVTLEKLRAELDEKETERSDMKETIFELEDEVEQ 700
701 HRAVKLHDNLIISDLENTVKKLQDQKHDMEREIKNLHRRLREESAEWRQF 750
751 QADLQTAVVIANDIKSEAQEEIGDLKRRLHEAQEKNEKLTKELEEIKSRK 800
801 QEEERGRVYNYMNAVERDLAALRQGMGLSRRSSTSSEPTPTVKTLIKSFD 850
851 SASQVPSPAAATIPRTPLSPSPMKTPPAAAVSPMQRHSISGPISASKPLA 900
901 TLTDKRPSYAEIPVQEHLLRTSSTSRPASLPRVPAMESAKSISVSRRSSE 950
951 EIKRDISAPDGASPASLMAMGTTSPQLSLSSSPTASVTPTTRSRIREERK 1000
1001 DPLSALAREYGGSKRNALLKWCQKKTEGYQNIDITNFSSSWNDGLAFCAV 1050
1051 LHTYLPAHIPYQELNSQDKRRNFTLAFQAAESVGIKSTLDINEMVRTERP 1100
1101 DWQNVMLYVTAIYKYFET 1118
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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