 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P53710 from www.uniprot.org...
The NucPred score for your sequence is 0.23 (see score help below)
1 PLQLVLVFSQGILNCCVAYNVGLPKAKIFSGPSSEQFGYAVQQFINPKGN 50
51 WLLVGSPWSGFPKNRMGDVYKCPVDLSTTTCEKLNLQTSTSMSNVTEMKT 100
101 NMSLGLTLTRNVGTGGFLTCGPLWAQQCGSQYYTTGVCSDVSPDFQLRTS 150
151 FAPAVQTCPSFIDVVVVCDESNSIYPWDAVKNFLEKFVQGLDIGPTKTQM 200
201 GLIQYANNPRVVFNLNTFKSKDEMIKATSQTFQYGGDLTNTFKAIQYARD 250
251 TAYSTAAGGRPGATKVMVVVTDGESHDGSKLKAVIDQCNKDNILRFGIAV 300
301 LGYLNRNALDTKNLIKEIKAIASIPTERHFFNVSDEADLLEKAGTIGEQI 350
351 FSIEGTVQGGDNFQMEMSQVGFSAEYSPQNNILMLGAVGAYDWSGTVVQK 400
401 TPHGHLIFSKQAFEQILQDRNHSSYLGYSVASISTGNSVHFVAGAPRANY 450
451 TGQIVLYSVNENGNVTVIQSQRGDQIGSYFGSVLCAVDVNKDTITDVLLV 500
501 GAPMYMNDLKKEEGRVYLFTITKGILNWHQFLEGPNGLENARFGSAIAAL 550
551 SDINMDGFNDVIVGSPLENQNSGAVYIYNGHEGMIRLRYSQKILGSDRAF 600
601 SSHLQYFGRSLDGYGDLNGDSITDVSVGAFGQVVQLWSQSIADVSVDASF 650
651 TPKKITLLNKNAEIKLKLCFSAKFRPTNQNNQVAIVYNITIDEDQFSSRV 700
701 ISRGLFKENNERCLQKTMIVSQAQRCSEYIIHIQEPSDIISPLNLCMNIS 750
751 LENPGTNPALEAYSETVKVFSIPFHKDCGDDGVCISDLVLNVQQLPATQQ 800
801 QPFIVSNQNKRLTFSVQLKNKKESAYNTEIVVDFSENLFFASWSMPVDGT 850
851 EVTCQIASSQKSVTCNVGYPALKSKQQVTFTINFDFNLQNLQNQASISFR 900
901 ALSESQEENMADNSVNLKLSLLYDAEIHITRSTNINFYEVSLDGNVSSVV 950
951 HSFEDIGPKFIFSIKVTTGSVPVSMASVIIHIPQYTKDKNPLMYLTGVHT 1000
1001 DQAGDISCEAEINPLKIGQTSSSVSFKSENFRHIKELNCRTASCSNIMCW 1050
1051 LRDLQVKGEYFLNVSTRIWNGTFAASTFQTVQLTAAAEIDTYNPQIYVIE 1100
1101 ENTVTIPLTIMKPHEKVEVPTGVIVGSVIAGILLLLALVAILWKLGFFKR 1150
1151 KYEKMAKNPDETDETTELNS 1170
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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