 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8H0U8 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MEVEKSKYRSEDLDVVEEEADLKKSRRDRDRSNERKKDKGSEKRREKDRR 50
51 KKRVKSSDSEDDYDRDDDEEREKRKEKERERRRRDKDRVKRRSERRKSSD 100
101 SEDDVEEEDERDKRRVNEKERGHREHERDRGKDRKRDREREERKDKERER 150
151 EKDRERREREREEREKERVKERERREREDGERDRREREKERGSRRNRERE 200
201 RSREVGNEESDDDVKRDLKRRRKEGGERKEKEREKSVGRSSRHEDSPKRK 250
251 SVEDNGEKKEKKTREEELEDEQKKLDEEVEKRRRRVQEWQELKRKKEEAE 300
301 SESKGDADGNEPKAGKAWTLEGESDDEEGHPEEKSETEMDVDEETKPEND 350
351 GDAKMVDLENETAATVSESGGDGAVDEEEIDPLDAFMNTMVLPEVEKFCN 400
401 GAPPPAVNDGTLDSKMNGKESGDRPKKGFNKALGRIIQGEDSDSDYSEPK 450
451 NDDDPSLDEDDEEFMKRVKKTKAEKLSLVDHSKIEYEPFRKNFYIEVKDI 500
501 SRMTQEEVNTYRKELELKVHGKDVPRPIKFWHQTGLTSKILDTMKKLNYE 550
551 KPMPIQTQALPIIMSGRDCIGVAKTGSGKTLGFVLPMLRHIKDQPPVEAG 600
601 DGPIGLVMAPTRELVQQIHSDIRKFSKPLGIRCVPVYGGSGVAQQISELK 650
651 RGTEIVVCTPGRMIDILCTSSGKITNLRRVTFLVMDEADRMFDMGFEPQI 700
701 TRIIQNIRPERQTVLFSATFPRQVETLARKVLNKPVEIQVGGRSVVNKDI 750
751 TQLVEVRPESDRFLRLLELLGEWSEKGKILVFVQSQEKCDALYRDMIKSS 800
801 YPCLSLHGGKDQTDRESTISDFKNDVCNLLIATSVAARGLDVKELELVVN 850
851 FDAPNHYEDYVHRVGRTGRAGRKGCAVTFISEDDAKYAPDLVKALELSEQ 900
901 PVPDDLKALADGFMVKVKQGIEQAHGTGYGGSGFKFNEEEEEVRKAAKKA 950
951 QAKEYGFEEDKSDSEDENDVVRKAGGGEISQQQATFAQIAAIAAAAKAAA 1000
1001 AAPVSAPVTANQLLANGGGLAAMPGVLPVTVPTLPSEGAGRAAAMVAAMN 1050
1051 LQHNLAKIQADAMPEHYEAELEINDFPQNARWKVTHKETLGPISEWTGAA 1100
1101 ITTRGQFYPTGRIPGPGERKLYLFIEGPSEKSVKHAKAELKRVLEDITNQ 1150
1151 AMSSLPGGASGRYSVL 1166
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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