| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P32849 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MSHIEQEERKRFFNDDLDTSETSLNFKSENKESFLFANSHNDDDDDVVVS 50
51 VSDTTEGEGDRSIVPVRREIEEEGQNQFITELLRIIPEMPKDLVMELNEK 100
101 FGSQEEGLSLALSHYFDHNSGTSISKIPSSPNQLNTLSDTSNSTLSPSSF 150
151 HPKRRRIYGFRNQTRLEDKVTWKRFIGALQVTGMATRPTVRPLKYGSQMK 200
201 LKRSSEEISATKVYDSRGRKKASMASLVRIFDIQYDREIGRVSEDIAQIL 250
251 YPLLSSHEISFEVTLIFCDNKRLSIGDSFILQLDCFLTSLIFEERNDGES 300
301 LMKRRRTEGGNKREKDNGNFGRTLTETDEELESRSKRLALLKLFDKLRLK 350
351 PILDEQKALEKHKIELNSDPEIIDLDNDEICSNQVTEVHNNLRDTQHEEE 400
401 TMNLNQLKTFYKAAQSSESLKSLPETEPSRDVFKLELRNYQKQGLTWMLR 450
451 REQEFAKAASDGEASETGANMINPLWKQFKWPNDMSWAAQNLQQDHVNVE 500
501 DGIFFYANLHSGEFSLAKPILKTMIKGGILSDEMGLGKTVAAYSLVLSCP 550
551 HDSDVVDKKLFDIENTAVSDNLPSTWQDNKKPYASKTTLIVVPMSLLTQW 600
601 SNEFTKANNSPDMYHEVYYGGNVSSLKTLLTKTKTPPTVVLTTYGIVQNE 650
651 WTKHSKGRMTDEDVNISSGLFSVNFYRIIIDEGHNIRNRTTVTSKAVMAL 700
701 QGKCKWVLTGTPIINRLDDLYSLVKFLELDPWRQINYWKTFVSTPFESKN 750
751 YKQAFDVVNAILEPVLLRRTKQMKDKDGKPLVELPPKEVVIKRLPFSKSQ 800
801 DLLYKFLLDKAEVSVKSGIARGDLLKKYSTILVHILRLRQVCCHPGLIGS 850
851 QDENDEDLSKNNKLVTEQTVELDSLMRVVSERFDNSFSKEELDAMIQRLK 900
901 VKYPDNKSFQSLECSICTTEPMDLDKALFTECGHSFCEKCLFEYIEFQNS 950
951 KNLGLKCPNCRNQIDACRLLALVQTNSNSKNLEFKPYSPASKSSKITALL 1000
1001 KELQLLQDSSAGEQVVIFSQFSTYLDILEKELTHTFSKDVAKIYKFDGRL 1050
1051 SLKERTSVLADFAVKDYSRQKILLLSLKAGGVGLNLTCASHAYMMDPWWS 1100
1101 PSMEDQAIDRLHRIGQTNSVKVMRFIIQDSIEEKMLRIQEKKRTIGEAMD 1150
1151 TDEDERRKRRIEEIQMLFE 1169
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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