 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O93918 from www.uniprot.org...
The NucPred score for your sequence is 0.30 (see score help below)
1 MAAPYRQPEEAVDDSEFIDDHHDHLRDTVHHRLRANSAIMQFQKILVANR 50
51 GEIPIRIFRTAHELSLQTVAIFSHEDRLSMHRQKADEAYMIGHRGQYTPV 100
101 GAYLAADEIVKIALEHGVHLIHPGYGFLSENADFARKVEKAGMVFVGPTP 150
151 DTIDSLGDKVSARQLAIRCNVPVVPGTEGPVERYEEVKAFTDTYGFPIII 200
201 KAAFGGGGRGMRVVRNQADLRDSFERATSEARSAFGNGTVFVERFLDKPK 250
251 HIEVQLLGDNHGNVVHLFERDCSVQRRHQKVVEVAPAKDLPTDVRDRILS 300
301 DAVKLAKSVNYRNAGTAEFLVDQQNRHYFIEINPRIQVEHTITEEITGID 350
351 IVAAQIQIAAGATLEQLGLTQDRISTRGFAIQCRITTEDPSKGFSPDTGK 400
401 IEVYRSAGGNGVRLDGGNGFAGAIITPHYDSMLVKCTCRGSTYEIARRKV 450
451 VRALVEFRIRGVKTNIPFLTSLLSHPTFVDGNCWTTFIDDTPELFALVGS 500
501 QNRAQKLLAYLGDVAVNGSSIKGQMGEPKFKGEIIKPKLLDAQGKPLDVS 550
551 QPCTKGWKQIIDQEGPVAFAKAVRANKGCLIMDTTWRDAHQSLLATRVRT 600
601 IDLLNIAHETSHALSNAYSLECWGGATFDVAMRFLYEDPWDRLRKMRKAV 650
651 PNIPFQMLLRGANGVAYSSLPDNAIYHFCKNAKKCGVDIFRVFDALNDID 700
701 QLEVGIKAVHAAEGVVEATVCYSGDMLNPKKKYNLEYYLALVDKIVALKP 750
751 HVLGIKDMAGVLKPQAARLLVGSIRERYPDLPIHVHTHDSAGTGVASMIA 800
801 CAQAGADAVDAATDSMSGMTSQPSIGAILASLEGTEHDPGLNSAHVRALD 850
851 SYWAQLRLLYSPFEANLTGPDPEVYEHEIPGGQLTNLIFQASQLGLGQQW 900
901 AETKKAYEVANDLLGDIVKVTPTSKVVGDLAQFIVSNKLSAQDVVDRAAE 950
951 LDFPGSVLEFLEGLMGQPFGGFPEPLRSRALRNRRKLDKRPGLYLEPLDL 1000
1001 AAIKNQIREQFGSATEYDVASYAMYPKVFEDYKKFVQKYGDLSVLPTRYF 1050
1051 LAKPEIGEEFHVELEKGKVLILKLLAIGPLSEQTGQREVFYEVNGEVRQV 1100
1101 SIDDKKASIDNTARPKADVGDSSQVGAPMSGVVVEIRVHDGLEVKKGDPL 1150
1151 AVLSAMKMEMVISAPHSGKVSGLLVKEGDSVDGQDLVCKITKA 1193
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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