 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10197 from www.uniprot.org...
The NucPred score for your sequence is 0.47 (see score help below)
1 MEEEESLEGIISIDESLITSRLSQILDDVLDDRSSSHSVEKLKDVAVKYL 50
51 QFCQFQPTLLDKLLSKYVPNLASYLLKVKNIGKCNSITVILYQFCKIRGY 100
101 KAVRVLFPVGVQYIKELYTLLNESSNNTWHFHYIVLLWLSQALNTPFPLN 150
151 SLDDSLDVKKTIYTIAIKYLENSGIDKEASCLVLSRLFSRDDGLDLLLGF 200
201 LHHCESSWFKRSIFYKIGCLFSLSSFLKICPRNDCLQTVDVAFQFLNVAR 250
251 EDLVGQENSALRKLLCKCYTRLGIVLLPVNSSPNWKYSISNPDSFFQLPD 300
301 DSNEEVHIYLEVIVDFLLSSVSDIDSFVRWSAAKGLAKIISRLPWNLAEQ 350
351 VIDAIIELMTENMFLNPIENTVNISITSPLVWHGAILFFAKLAGAGLIKY 400
401 SKCLHILPLIEVGLSYEVRYGTRVTGQSIRDASCYFVWSFYHCYSKSAIE 450
451 GLQTNLILCLLQTVLFDNEINVRRAATAALFEVIGRHASIPDGLSLISHI 500
501 NYVSVTDISNCYGDLCMKVAHFPQFRSCVFQRLFTNLQHWDVKVQQLSAF 550
551 SLRQLSIKYPKELSIYLPPILDYLSVGNADFIFGYTIGLASIIGGFLSIS 600
601 FPFDINRIHDLLSHKNLLSLKKFSRQQQTKIILGILKGIQQIFANDIRVD 650
651 RAFFSEAFSVIIAAIDLQEETIIKDISDAYSVLVKFDDMEETLEVLLDYI 700
701 RKCSTSKEARIVYIILQNLPNISFRYQKKICKLLLDIYPQLHSIDYQAPV 750
751 ANALQNIIPFTYEKTESIEEFVKELLQVCSNYLTDTRGDVGSWIRKPAMK 800
801 AISSLLVKDSSGKKLSEDIVWCCISYIIRQTFDKIDSLRGLAYQALEQIR 850
851 VHYLIRRCEALTNIINRIRNNPNMDGEVLNELNISLLEIPNLRLQAFYGI 900
901 TVFTADGFGSDLAVKCFEFYLSYVYQLEDSFKKSNSRYGKRDLLQLYIDI 950
951 LSSEDEIARFYFPIMKSFTSLLAYGCFTDFQNVKGMSKAIFIVQRRALTC 1000
1001 KSPGGLSAILELYRTLFLSKNELLRHHALKYTANLLLNPIEKVRYQAADT 1050
1051 LLYAKSIGLLTFLPNELNQKLLTLDWFVPVSQNATFVKQLRNIIQKQIDK 1100
1101 LIADR 1105
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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