 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O42643 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MDDLKELEYLSLVSKVASEIRNHTGIDDNTLAEFIINLHDQSKNYDEFKN 50
51 NVLSCGGEFTDSFLQNISRLIKEIKPKDDIPTDNVNNGSNSVNGASHDLD 100
101 SKDVDKQHQRKMFPGLSIPNSTNNLRDRPALMDNAMDELEELSTLAKTRR 150
151 NDRDSRRDERHYLNGIRERRERSISPSFSHHSRTSISGQSHSSRSSRGPL 200
201 LNAPTLYGIYSGVVSGIKDFGAFVTLDGFRKRTDGLVHISNIQLNGRLDH 250
251 PSEAVSYGQPVFVKVIRIDESAKRISLSMKEVNQVTGEDLNPDQVSRSTK 300
301 KGSGANAIPLSAQNSEIGHVNPLETFTSNGRKRLTSPEIWELQQLAASGA 350
351 ISATDIPELNDGFNTNNAAEINPEDDEDVEIELREEEPGFLAGQTKVSLK 400
401 LSPIKVVKAPDGSLSRAAMQGQILANDRREIRQKEAKLKSEQEMEKQDLS 450
451 LSWQDTMSNPQDRKFAQDVRDSAARQLTSETPSWRQATRNANISYGKRTT 500
501 LSMKEQREGLPVFKLRKQFLEAVSKNQILVLLGETGSGKTTQITQYLAEE 550
551 GYTSDSKMIGCTQPRRVAAMSVAKRVAEEVGCRVGEEVGYTIRFEDKTSR 600
601 MTQIKYMTDGMLQRECLVDPLLSKYSVIILDEAHERTVATDVLFGLLKGT 650
651 VLKRPDLKLIVTSATLDAERFSSYFYKCPIFTIPGRSYPVEIMYTKQPEA 700
701 DYLDAALMTVMQIHLSEGPGDILVFLTGQEEIDTSCEILYERSKMLGDSI 750
751 PELVILPVYSALPSEIQSRIFEPAPPGGRKVVIATNIAETSLTIDGIYYV 800
801 VDPGFVKQSCFDPKLGMDSLIVTPISQAQARQRSGRAGRTGPGKCYRLYT 850
851 ESAYRNEMLPSPIPEIQRQNLSHTILMLKAMGINDLLNFDFMDPPPAQTM 900
901 IAALQNLYALSALDDEGLLTPLGRKMADFPMEPQLSKVLITSVELGCSEE 950
951 MLSIIAMLSVPNIWSRPREKQQEADRQRAQFANPESDHLTLLNVYTTWKM 1000
1001 NRCSDNWCYEHYIQARGMRRAEDVRKQLIRLMDRYRHPVVSCGRKRELIL 1050
1051 RALCSGYFTNVAKRDSHEGCYKTIVENAPVYMHPSGVLFGKAAEWVIYHE 1100
1101 LIQTSKEYMHTVSTVNPKWLVEVAPTFFKFANANQVSKTKKNLKVLPLYN 1150
1151 RFEKPDEWRISKQRKGGR 1168
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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