 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P33327 from www.uniprot.org...
The NucPred score for your sequence is 0.60 (see score help below)
1 MLFDNKNRGALNSLNTPDIASLSISSMSDYHVFDFPGKDLQREEVIDLLD 50
51 QQGFIPDDLIEQEVDWFYNSLGIDDLFFSRESPQLISNIIHSLYASKLDF 100
101 FAKSKFNGIQPRLFSIKNKIITNDNHAIFMESNTGVSISDSQQKNFKFAS 150
151 DAVGNDTLEHGKDTIKKNRIEMDDSCPPYELDSEIDDLFLDNKSQKNCRL 200
201 VSFWAPESELKLTFVYESVYPNDDPAGVDISSQDLLKGDIESISDKTMYK 250
251 VSSNENKKLYGLLLKLVKEREGPVIKTTRSVENKDEIRLLVAYKRFTTKR 300
301 YYSALNSLFHYYKLKPSKFYLESFNVKDDDIIIFSVYLNENQQLEDVLLH 350
351 DVEAALKQVEREASLLYAIPNNSFHEVYQRRQFSPKEAIYAHIGAIFINH 400
401 FVNRLGSDYQNLLSQITIKRNDTTLLEIVENLKRKLRNETLTQQTIINIM 450
451 SKHYTIISKLYKNFAQIHYYHNSTKDMEKTLSFQRLEKVEPFKNDQEFEA 500
501 YLNKFIPNDSPDLLILKTLNIFNKSILKTNFFITRKVAISFRLDPSLVMT 550
551 KFEYPETPYGIFFVVGNTFKGFHIRFRDIARGGIRIVCSRNQDIYDLNSK 600
601 NVIDENYQLASTQQRKNKDIPEGGSKGVILLNPGLVEHDQTFVAFSQYVD 650
651 AMIDILINDPLKENYVNLLPKEEILFFGPDEGTAGFVDWATNHARVRNCP 700
701 WWKSFLTGKSPSLGGIPHDEYGMTSLGVRAYVNKIYETLNLTNSTVYKFQ 750
751 TGGPDGDLGSNEILLSSPNECYLAILDGSGVLCDPKGLDKDELCRLAHER 800
801 KMISDFDTSKLSNNGFFVSVDAMDIMLPNGTIVANGTTFRNTFHTQIFKF 850
851 VDHVDIFVPCGGRPNSITLNNLHYFVDEKTGKCKIPYIVEGANLFITQPA 900
901 KNALEEHGCILFKDASANKGGVTSSSMEVLASLALNDNDFVHKFIGDVSG 950
951 ERSALYKSYVVEVQSRIQKNAELEFGQLWNLNQLNGTHISEISNQLSFTI 1000
1001 NKLNDDLVASQELWLNDLKLRNYLLLDKIIPKILIDVAGPQSVLENIPES 1050
1051 YLKVLLSSYLSSTFVYQNGIDVNIGKFLEFIGGLKREAEASA 1092
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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