 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P59672 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MGKEQELLEAARTGHLPAVEKLLSGKRLSSGFGGGGGGSGSGGGSGGGGL 50
51 GSSSHPLSSLLSMWRGPNVNCVDSTGYTPLHHAALNGHRDVVEVLLRNDA 100
101 LTNVADSKGCYPLHLAAWKGDAQIVRLLIQQGPSHTRVNEQNALEIRELK 150
151 KYGPFDPYINAKNNDNETALHCAAQYGHTEVVKALLEELTDPTMRNNKFE 200
201 TPLDLAALYGRLEVVKLLLGAHPNLLSCSTRKHTPLHLAARNGHKAVVQV 250
251 LLDAGMDSNYQTEMGSALHEAALFGKTDVVQILLAAGIDVNIKDNRGLTA 300
301 LDTVRDLPSQKSQQIAALIEDHMTGKRSVKEVDRTSTAQLPLLSNTDAIA 350
351 PMSQGSMEKTVTELILHFDTHADEEGPYEALYNAVSCHSLDSTASGRSSD 400
401 RDSMNKEAEATGTRAAGVRPRERPPPPAKPPPDEEEEERVDKKYFPLAAS 450
451 EGLAVRPRIQSSAPQEEEEHPYELLLTAETKKLGTTDGRTEDHRQSGSGR 500
501 SQDSVEGQDGQVPEQFSGLLHGSSPVCEVGQDPFQLLTAPSQSHPESSQQ 550
551 DACHEASMQLEEPGVQGTEPPQPGVPDQSKRVGLPAGLTALASRTYLDAL 600
601 THTVPLRPAGAEEEDQSGPRSRAPPTSKPKAELKLSRSLSKSDSDLLTCS 650
651 PTEDATMGSRSESLSNCSIGKKRLEKSPSFASEWDEIEKIMSSIGEGIDF 700
701 SQEQQKISGSRTLEQSVGEWLESIGLQQYESKLLLNGFDDVRFLGSNVME 750
751 EQDLREIGISDPQHRRKLLQAARSLPKVKALGYDGVSPTSVPSWLDSLGL 800
801 QDYVHSFLSSGYSSIDTVKNLWELELVNVLKVHLLGHRKRIIASLADRPY 850
851 EEPPQKPPRFSQLRCQDLISQTSSPLSQNDSCTGRSADLLLPSADTSRRR 900
901 HDSLPDPGTASRADRFRVQEEPSETKLTLRPPSLAAPYAPVQSWQHQPEK 950
951 LIFESCGYEANYLGSMLIKDLRGTESTQDACAKMRKSTEHMKKIPTIILS 1000
1001 ITYKGVKFIDASNKNVIAEHEIRNISCAAQDPEDLCTFAYITKDLQTSHH 1050
1051 YCHVFSTVDVNLTYEIILTLGQAFEVAYQLALQAQKSRTMAASAASMIET 1100
1101 KSSKPVPKPRVGMRKSALEPPDSDQEAPSHASVSWIVDPKPDSKRSLSTN 1150
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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