 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P38989 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MKVEELIIDGFKSYATRTVITDWDPQFNAITGLNGSGKSNILDAICFVLG 50
51 IASMSTVRASSLQDLIYKRGQAGVTKASVTIVFDNTDKSNSPIGFTNSPQ 100
101 ISVTRQVVLGGTSKYLINGHRAPQQSVLQLFQSVQLNINNPNFLIMQGKI 150
151 TKVLNMKPSEILSLIEEAAGTKMFEDRREKAERTMSKKETKLQENRTLLT 200
201 EEIEPKLEKLRNEKRMFLEFQSTQTDLEKTERIVVSYEYYNIKHKHTSIR 250
251 ETLENGETRMKMLNEFVKKTSEEIDSLNEDVEEIKLQKEKELHKEGTISK 300
301 LENKENGLLNEISRLKTSLSIKVENLNDTTEKSKALESEIASSSAKLIEK 350
351 KSAYANTEKDYKMVQEQLSKQRDLYKRKEELVSTLTTGISSTGAADGGYN 400
401 AQLAKAKTELNEVSLAIKKSSMKMELLKKELLTIEPKLKEATKDNELNVK 450
451 HVKQCQETCDKLRARLVEYGFDPSRIKDLKQREDKLKSHYYQTCKNSEYL 500
501 KRRVTNLEFNYTKPYPNFEASFVHGVVGQLFQIDNDNIRYATALQTCAGG 550
551 RLFNVVVQDSQTATQLLERGRLRKRVTIIPLDKIYTRPISSQVLDLAKKI 600
601 APGKVELAINLIRFDESITKAMEFIFGNSLICEDPETAKKITFHPKIRAR 650
651 SITLQGDVYDPEGTLSGGSRNTSESLLVDIQKYNQIQKQIETIQADLNHV 700
701 TEELQTQYATSQKTKTIQSDLNLSLHKLDLAKRNLDANPSSQIIARNEEI 750
751 LRDIGECENEIKTKQMSLKKCQEEVSTIEKDMKEYDSDKGSKLNELKKEL 800
801 KLLAKELEEQESESERKYDLFQNLELETEQLSSELDSNKTLLHNHLKSIE 850
851 SLKLENSDLEGKIRGVEDDLVTVQTELNEEKKRLMDIDDELNELETLIKK 900
901 KQDEKKSSELELQKLVHDLNKYKSNTNNMEKIIEDLRQKHEFLEDFDLVR 950
951 NIVKQNEGIDLDTYRERSKQLNEKFQELRKKVNPNIMNMIENVEKKEAAL 1000
1001 KTMIKTIEKDKMKIQETISKLNEYKRETLVKTWEKVTLDFGNIFADLLPN 1050
1051 SFAKLVPCEGKDVTQGLEVKVKLGNIWKESLIELSGGQRSLIALSLIMAL 1100
1101 LQFRPAPMYILDEVDAALDLSHTQNIGHLIKTRFKGSQFIVVSLKEGMFA 1150
1151 NANRVFRTRFQDGTSVVSIM 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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