 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9QZW0 from www.uniprot.org...
The NucPred score for your sequence is 0.28 (see score help below)
1 MFRRTLNRLCAGEEKRVGTRTVFVGNHPISGTEPYIAQRFCDNRIVSSKY 50
51 TLWNFLPKNLFEQFRRIANFYFLIIFLVQVTVDTPTSPVTSGLPLFFVIT 100
101 VTAIKQGYEDWLRHRADNEVNKSAVYIIENAKRVRKESEKIKVGDVVEVQ 150
151 ANETFPCDLILLSSCTTDGTCYVTTASLDGESNCKTHYAVRDTIALCTAE 200
201 SIDNLRATIECEQPQPDLYRFVGRISIYSNSIEAVARSLGPENLLLKGAT 250
251 LKNTKKIYGVAVYTGMETKMALNYQGKSQKCSAVEKSINAFLIVYLFILL 300
301 TKAAVCTTLKYVWQSSPYNDEPWYNQKTQKERETFQVLKMFTDFLSFMVL 350
351 FNFIIPVSMYVTVEMQKFLGSFFISWDKDFFDEEINEGALVNTSDLNEEL 400
401 GQVDYVFTDKTGTLTENSMEFIECCIDGHKYKGTTQEVDGLSQTDGPLAY 450
451 FDKADKNREALFLRALCLCHTVEMKTNDDVDGPVEGAGFTYISSSPDEIA 500
501 LVKGAKRFGFTFLGNQNGYIRVENQRKEIEEYELLHTLNFDSVRRRMSVI 550
551 VRTQKGDILLFCKGADSSIFPRVHSHQIELTKDHVERNAMDGYRTLCVAF 600
601 KEIPPDDFERINAQLVEAKMALQDREEKLEKVFDEIETNMNLIGATAVED 650
651 KLQDQAAETIEALHAAGLKVWVLTGDKMETAKSTCYACRLFQTNTELLEL 700
701 TTKTIEESERKEDRLHELLIEYRKKLLHEFPKSTRSLKKAWTEHQEYGLI 750
751 IDGSTLSLILNSSQDCSSNNYKSIFLQICMKCTAVLCCRMAPLQKAQIVR 800
801 MVKNLKGSPITLSIGDGANDVSMILESHVGIGIKGKEGRQAARNSDYSVP 850
851 KFKHLKKLLLVHGHLYYVRIAHLVQYFFYKNLCFILPQFLYQFFCGFSQQ 900
901 PLYDAAYLTMYNICFTSLPILAYSLLEQHINIDTLTADPRLYMKITGNAM 950
951 LQLGPFLHWTFLAAFEGTVFFFGTYFLFQTSSLEDNGKIYGNWTFGTIVF 1000
1001 TVLVFTVTLKLALDTRFWTWINHFVIWGSLAFYVFFSFFWGGIIWPFLKQ 1050
1051 QRMYFVFAQMLCSVSTWLAIILLIFISLFPEILLIVVKNVRRRSARRNLS 1100
1101 CRRASDSLSARPSVRPLLLRTFSDESNIL 1129
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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