 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P34498 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MSDKRADGRLEGTSDTFGGLVIKKKKVEGDSKPTEPSGKSLLGLDRLAST 50
51 KREHARKRLEDDDDRGVTESVRKGIEKVHEKHRDRDDRGMKYKSRDDDRR 100
101 RDRDRSERREPSSRRGWKDRSGDQTPRFKVPDTPSRMSWDQDDREGSSRK 150
151 RNSWDMPTPRGERDRKRYMDSERSISSAWRSERRNRDDEKRRRHRKPEDS 200
201 VRSVKEEKAEPTFHDDEERAQWEEEQKNLDREWYDNEGAFDDEYNPFNKV 250
251 SDEFVEKREKQWQEKTQKPRLTVKQQAIKRENELWENNRLHRSGVVAMAD 300
301 ELSSIFEDETDENRVTILVQNIVPPFLDGRIVFTKQAQPIIPVVDTTCDM 350
351 AVSAARGSVAVRKRREVEDRKKAQDKHWELAGSKLGNLMGVKEKKDETAD 400
401 PEDDDSGNYKESHQFASHMKDNEAVSDFAMEKSIKQQREYLPVFACRQKM 450
451 MNVIRENNVVIIVGETGSGKTTQLAQYLLEDGFGDSGLIGCTQPRRVAAM 500
501 SVARRVADEMGVDLGQDVGYAIRFEDCTSEKTIIKYMTDGILLRECLGDG 550
551 SLDQYSAIIMDEAHERSLNTDVLFGLLREVIAKRADLKLIVTSATMDADK 600
601 FADFFGGNCPTFTIPGRTFPVELFHARTPVEDYVDAAVKQAVTIHLGGMD 650
651 GDILIFMPGQEDIECTCEMIKEKLGELDEAPPLAVLPIYSQLPSDLQAKI 700
701 FQRAPGGMRKAIVATNIAETSLTVDGILFVIDPGFCKMKVYNPRIGMDAL 750
751 SIFPVSQASANQRTGRAGRTGPGQCYRLYTERQFKDELLKSTVPEIQRTN 800
801 LANVVLLLKSLGVDDLLKFHFMDAPPQDNMLNSMYQLWTLGALDNTGQLT 850
851 PMGRKMVEFPLDPTLSKMLIMSAEMGCSDEVLTIVSMLSVPAIFFRPKGR 900
901 EEEADAKKEKFQVPESDHLTFLNVYIQWRTHKYSAKWCADNYLHVKALKK 950
951 VREVRAQLKEIMQDLKLPLISNGSEWDIVRKCICSAYFHNAARLKGIGEY 1000
1001 VNVRTGIPCFLHPTSALFGMGFMPDYVVYHELIMTAKEYMQCVTAVDAIW 1050
1051 LAELGPMFYSIKESKQSRKELKMESVRTVETMEAEMREAQKEMERRKEES 1100
1101 DKAFKRPESSRRVVEVGSKSARSERRKLWGL 1131
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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