 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P33530 from www.uniprot.org...
The NucPred score for your sequence is 0.41 (see score help below)
1 MSSSRPSQSSTTSARSKHSARIIAQTTIDAKLHADFEESGDSFDYSSSVR 50
51 VTSVAGDERKPKSDRVTTAYLNQIQKGKFIQPFGCLLALDEKTFKVIAFS 100
101 ENAPEMLTMVSHAVPSVGELPALGIGTDIRTIFTGPSAAALQKALGFGEV 150
151 SLLNPVLVHCKTSGKPYYAIVHRVTGSLIIDFEPVKPYEVPMTAAGALQS 200
201 YKLAAKAITRLQALPSGSMERLCDTMVQEVFELTGYDRVMTYKFHDDDHG 250
251 EVVAEITKPGLDPYLGLHYPATDIPQAARFLFMKNKVRMICDCRAKHVKV 300
301 VQDEKLPFDLTLCGSTLRAPHYCHLQYMENMSSIASLVMAVVVNDGDEEG 350
351 ESSDSTQSQKRKRLWGLVVCHNTTPRFVPFPLRYACEFLAQVFAIHVNKE 400
401 LELESQILEKNILRTQTLLCDMLMRVAPLGIVSQSPNIMDLVKCDGAALL 450
451 YKNKIHRLGMTPSDFQLHDIVSWLSEYHTDSTGLSTDSLYDAGFPGALAL 500
501 GDVVCGMAAVRISDKGWLFWYRSHTAAEVRWGGAKHEPGEKDDGRKMHPR 550
551 SSFKAFLEVVKTRSVPWKDYEMDAIHSLQLILRNASKDADAMDSNTNIIH 600
601 TKLNDLKIDGLQELEAVTAEMVRLIETASVPIFAVDVDGQLNGWNTKIAE 650
651 LTGLPVDEAIGNHLLTLVEDSSVDTVSKMLELALQGKEERNVEFEIKTHG 700
701 PSGDSSPISLIVNACASRDVGDSVVGVCFIAQDITGQKNIMDKFTRIEGD 750
751 YRAIIQNPHPLIPPIFGTDQFGWCSEWNSAMTKLTGWRRDDVIDKMLLGE 800
801 VFGTQAACCRLKNQEAFVNFGVVLNNAMTGQECAKISFGFFARNGKYVEC 850
851 LLCVSKRLDREGAVTGLFCFLQLASHELQQALHIQRLSEQTALKRLKVLA 900
901 YIRRQIRNPLSGIIFSRKMLEGTNLGEEQKNILRTSSQCQRQLNKILDDT 950
951 DLDSIIDGYLDLEMLEFKLHEVLVASISQIMMKSNGKNIMIVNDMVEDLL 1000
1001 NETLYGDSPRLQQVLANFLLVCVNSTPSGGQLSISGTLTKDRIGESVQLA 1050
1051 LLEVRISHTGGGVPEELLSQMFGTEAEASEEGISLLISRKLVKLMNGEVQ 1100
1101 YLREAGRSTFIISVELAVATKSSC 1124
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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