 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UUS2 from www.uniprot.org...
The NucPred score for your sequence is 0.20 (see score help below)
1 MTVSTHHDDSPGLSGRLRDLLHHVFGNQKSPTVYPNAPGNSAKPVPTGLA 50
51 DDIDKLGFKDIDTLLIFLNSAVKGVNDDQQFLLEKMIQLLAKLPPASREG 100
101 KKLTDGLINDLWDSLDHPPVASLGKGFSFREPDGSNNNIHLPSLGAANTP 150
151 YARSTKPLVFQNPNPPDPATIFDTLMVRDPAKFRPHPNKISSMLFYLATI 200
201 ITHDIFQTSPRDFNINLTSSYLDLSPLYGRNHDEQMAVRTGKDGLLKPDT 250
251 FSSKRVIGFPPGVGAFLIMFNRFHNYVVTQLAKINEGGRFKRPTTPDDTA 300
301 GWETYDNSLFQTGRLITCGLYINIVLGDYVRTILNLNRANTTWNLDPRTK 350
351 EGKSLLSKPTPEAVGNQVSVEFNLIYRWHCTISERDDKWTTNAMREALGG 400
401 QDPATAKMEDVMRALGMFEKNIPDEPEKRTLAGLTRQSDGAFDDTELVKI 450
451 LQESIEDVAGAFGPNHVPACMRAIEILGIKQSRTWNVATLNEFRQFIGLT 500
501 PHDSFYHMNPDPKICKILAQMYDSPDAVELYPGIMAEAAKPPFSPGSGLC 550
551 PPYTTSRAILSDAVSLVRGDRFYTVDYTPRNITNWGFNEASTDKAVDWGH 600
601 VIYKLFFRAFPNHFLPNSVYAHFPFVVPSENKLIFEGLGAANKYSWDPPK 650
651 ARAPIQFIRSHKAVLEVLSNQKDYKVTWGPAIKMLSGDPATSFALAGDEP 700
701 ANAASRHHVIAALTAPKQWRDEVRRFYEVTTRDLLRRHGAPVHGVGAGPR 750
751 THEVDVIRDVIGLAHARFMASLFSLPLKEEGKEEGAYGEHELYRSLVTIF 800
801 AAIFWDSDVCNSLKLHQASKAAADKMSALIAEHVREMEAGTGFLGALGKL 850
851 KDLITGNDVHANGNGVYTNGNGVYTNGNGVHTNGNGVHTNGNGVPHAAPS 900
901 LRSFGDQLLQRMLSQDGRSIEETVSGTILPVVMAGTANQTQLLAQCLDYY 950
951 LGVGEKHLPEMKRLAMLNTSEADEKLLKYTMEGCRIRGCVALYRAVVTDQ 1000
1001 AVDDTIPCIPNKDDPTFARPLSNPQVAESARTLKLSTGTRMLVDLTTASH 1050
1051 DPAAFPDPDEVRLDRPLESYVHFGLGPHRCAGEPISQIALSSVMKVLLQL 1100
1101 DGLRRAAGPRGEIRSYPASQWPGQAGRPPRDPAWSGLRTFTSADQSAFSP 1150
1151 LATTMKINWEGRGDL 1165
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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