 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P11154 from www.uniprot.org...
The NucPred score for your sequence is 0.30 (see score help below)
1 MSQRKFAGLRDNFNLLGEKNKILVANRGEIPIRIFRTAHELSMQTVAIYS 50
51 HEDRLSTHKQKADEAYVIGEVGQYTPVGAYLAIDEIISIAQKHQVDFIHP 100
101 GYGFLSENSEFADKVVKAGITWIGPPAEVIDSVGDKVSARNLAAKANVPT 150
151 VPGTPGPIETVEEALDFVNEYGYPVIIKAAFGGGGRGMRVVREGDDVADA 200
201 FQRATSEARTAFGNGTCFVERFLDKPKHIEVQLLADNHGNVVHLFERDCS 250
251 VQRRHQKVVEVAPAKTLPREVRDAILTDAVKLAKECGYRNAGTAEFLVDN 300
301 QNRHYFIEINPRIQVEHTITEEITGIDIVAAQIQIAAGASLPQLGLFQDK 350
351 ITTRGFAIQCRITTEDPAKNFQPDTGRIEVYRSAGGNGVRLDGGNAYAGT 400
401 IISPHYDSMLVKCSCSGSTYEIVRRKMIRALIEFRIRGVKTNIPFLLTLL 450
451 TNPVFIEGTYWTTFIDDTPQLFQMVSSQNRAQKLLHYLADVAVNGSSIKG 500
501 QIGLPKLKSNPSVPHLHDAQGNVINVTKSAPPSGWRQVLLEKGPAEFARQ 550
551 VRQFNGTLLMDTTWRDAHQSLLATRVRTHDLATIAPTTAHALAGRFALEC 600
601 WGGATFDVAMRFLHEDPWERLRKLRSLVPNIPFQMLLRGANGVAYSSLPD 650
651 NAIDHFVKQAKDNGVDIFRVFDALNDLEQLKVGVDAVKKAGGVVEATVCF 700
701 SGDMLQPGKKYNLDYYLEIAEKIVQMGTHILGIKDMAGTMKPAAAKLLIG 750
751 SLRAKYPDLPIHVHTHDSAGTAVASMTACALAGADVVDVAINSMSGLTSQ 800
801 PSINALLASLEGNIDTGINVEHVRELDAYWAEMRLLYSCFEADLKGPDPE 850
851 VYQHEIPGGQLTNLLFQAQQLGLGEQWAETKRAYREANYLLGDIVKVTPT 900
901 SKVVGDLAQFMVSNKLTSDDVRRLANSLDFPDSVMDFFEGLIGQPYGGFP 950
951 EPFRSDVLRNKRRKLTCRPGLELEPFDLEKIREDLQNRFGDVDECDVASY 1000
1001 NMYPRVYEDFQKMRETYGDLSVLPTRSFLSPLETDEEIEVVIEQGKTLII 1050
1051 KLQAVGDLNKKTGEREVYFDLNGEMRKIRVADRSQKVETVTKSKADMHDP 1100
1101 LHIGAPMAGVIVEVKVHKGSLIKKGQPVAVLSAMKMEMIISSPSDGQVKE 1150
1151 VFVSDGENVDSSDLLVLLEDQVPVETKA 1178
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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