 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Y2Q0 from www.uniprot.org...
The NucPred score for your sequence is 0.27 (see score help below)
1 MPTMRRTVSEIRSRAEGYEKTDDVSEKTSLADQEEVRTIFINQPQLTKFC 50
51 NNHVSTAKYNIITFLPRFLYSQFRRAANSFFLFIALLQQIPDVSPTGRYT 100
101 TLVPLLFILAVAAIKEIIEDIKRHKADNAVNKKQTQVLRNGAWEIVHWEK 150
151 VAVGEIVKVTNGEHLPADLISLSSSEPQAMCYIETSNLDGETNLKIRQGL 200
201 PATSDIKDVDSLMRISGRIECESPNRHLYDFVGNIRLDGHGTVPLGADQI 250
251 LLRGAQLRNTQWVHGIVVYTGHDTKLMQNSTSPPLKLSNVERITNVQILI 300
301 LFCILIAMSLVCSVGSAIWNRRHSGKDWYLNLNYGGASNFGLNFLTFIIL 350
351 FNNLIPISLLVTLEVVKFTQAYFINWDLDMHYEPTDTAAMARTSNLNEEL 400
401 GQVKYIFSDKTGTLTCNVMQFKKCTIAGVAYGHVPEPEDYGCSPDEWQNS 450
451 QFGDEKTFSDSSLLENLQNNHPTAPIICEFLTMMAVCHTAVPEREGDKII 500
501 YQAASPDEGALVRAAKQLNFVFTGRTPDSVIIDSLGQEERYELLNVLEFT 550
551 SARKRMSVIVRTPSGKLRLYCKGADTVIYDRLAETSKYKEITLKHLEQFA 600
601 TEGLRTLCFAVAEISESDFQEWRAVYQRASTSVQNRLLKLEESYELIEKN 650
651 LQLLGATAIEDKLQDQVPETIETLMKADIKIWILTGDKQETAINIGHSCK 700
701 LLKKNMGMIVINEGSLDGTRETLSRHCTTLGDALRKENDFALIIDGKTLK 750
751 YALTFGVRQYFLDLALSCKAVICCRVSPLQKSEVVEMVKKQVKVVTLAIG 800
801 DGANDVSMIQTAHVGVGISGNEGLQAANSSDYSIAQFKYLKNLLMIHGAW 850
851 NYNRVSKCILYCFYKNIVLYIIEIWFAFVNGFSGQILFERWCIGLYNVMF 900
901 TAMPPLTLGIFERSCRKENMLKYPELYKTSQNALDFNTKVFWVHCLNGLF 950
951 HSVILFWFPLKALQYGTAFGNGKTSDYLLLGNFVYTFVVITVCLKAGLET 1000
1001 SYWTWFSHIAIWGSIALWVVFFGIYSSLWPAIPMAPDMSGEAAMLFSSGV 1050
1051 FWMGLLFIPVASLLLDVVYKVIKRTAFKTLVDEVQELEAKSQDPGAVVLG 1100
1101 KSLTERAQLLKNVFKKNHVNLYRSESLQQNLLHGYAFSQDENGIVSQSEV 1150
1151 IRAYDTTKQRPDEW 1164
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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