 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P53280 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MTKKKAATNYAERQNLASEDSSGDSVHFKDFIPLQELLKDKNYVPSVENL 50
51 EKILYNETMFNDQKICSNLLLEALIITLFTTISGKSALRLIQTSSLKERK 100
101 SWAQSFENNSSSYASIVLSWKDNDILLLKFLRFLLANKTAPLQINRYNLP 150
151 EYKLPLSFLIVSKITIPSILLNETYNLLKDYLYSITGRIESLISCSSTFD 200
201 KPALVVRKILKDYNRMIECRNFYFWYSFNAENRVNLTFSDNISLLMENDE 250
251 GNAGSGLDDSRFDHQKQPREAIMGRTINDQEQIYSFELNQDGTLEIPNVM 300
301 EHSLLRHELLFKILNLTTVLTPLLELQFSTLCGLVDPLMQPTPNDKHIIS 350
351 IDFLFQLFLGLMSQSIKTSQEHNDHYDWKFYMCFNMQKIIDATMLRLNCF 400
401 DFDILNSVNNTDNAVHWKTQLHRWLPHGLNTQDLELLYMIDILAVYTIYK 450
451 LYEKIPIQLNPFLFSLISLWKNLSCVILLALEIDRIEEENGTYETPLMVR 500
501 ATIRGAAALRSVIATVLNGLVKNNDHDFKHESLNTFMSPYGRKLCHGALY 550
551 ADLRSHTASLLALGASIEDVTDLFADLQSGDRFDEDIRYMFDYECEDYDE 600
601 SFSESDHGGLDESVVNPTEKIASGSNNVFFRRRCNCIFNDDKLVAEDGAN 650
651 EAFGSTNSENVEGAMHNNRNAVHNATTATSDHVVTSPNPLSVRSRSTFEF 700
701 DYSGEDWRDVPRDFNMYYSPSYSFIHEPKLDVIFSLTLRGATEKLNKEES 750
751 ILLVRSVASCVRNEQDQMILADLESNFSASINGDVEGEGNTKMSKIDNED 800
801 LRRTTPDDIYEIWSEESAFERMLNVNHDVAWRLMDEMLMCTGYRRILIWF 850
851 LTHLELKHSLIYYVFELIMGLRGKPFSGEASDQDKKDDMIYEILKKKQKN 900
901 EDASGLPFSRQGPIVLSDIETKMLLQEFFMNAAIFLSSKNNEEENEDGEK 950
951 ISLYSLGLVRLICYMVQTLIANDKFFFTKSECTFELQTLLMTWIGILPEA 1000
1001 KDLFFKIKTRLAMEEEDSADTMQHEGRKNSDIEKKLNAKPASELNLKLLN 1050
1051 LFPSKPANKDDSSPINTLRSFIADYSFDTQVNPPGRRVVFYDGKILPLPK 1100
1101 ADKPIPLHEYITLAELDVGDSE 1122
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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