 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q07553 from www.uniprot.org...
The NucPred score for your sequence is 0.60 (see score help below)
1 MPGPCASAAAFSCILVLLLLGCQRSNPLAAGATVSSMRRLTDTINIGFLA 50
51 EYSQMRVTLGGLPLAIEDVNKNPNLLPGKKLAFKPVDIGHKMSAYRVKPL 100
101 RAMTQMREAGVTAFIGPDESCTTEALLASAWNTPMLSFKCSDPIVSNKST 150
151 FHTFARTLAPASKVSKSVISLLNAFHWNKFSIVVSSKPIWGSDVARAIQE 200
201 LAEARNFTISHFKYISDYIPTTKTLSQIDKIIEETYATTRIYVFIGEHIA 250
251 MVDFVRGLQNRRLLESGDYIVVSVDDEIYDSNRRVNIMERNYLDPYIRKE 300
301 KSKSLDKISFRSVIKISMTYPQNPHIRVPIYGLHLYDSVMIYVRAITEVL 350
351 RLGGDIYDGNLVMSHIFNRSYHSIQGFDVYIDSNGDAEGNYTVITLQNDV 400
401 GSGASIGSLAKMSMQPVGFFAYDKNSVIPEFRYIKNDRPIQWLNGRPPLA 450
451 EPLCGFHGELCPRKKLDWRYLVSGPLCALVVVVAIALLIKHYRYEQTLAG 500
501 LLWKVDMKDVTVINLGEYNNPTNKNIFQICRQSILVVGEPNKRSFTNIAL 550
551 FRGNIVAMKKIHKKSVDITRSIRKELKLMREVRHENIINFIGASTDHGSV 600
601 IIFTTYCARGSLEDVLANEDLHLDHMFISSLVSDILKGMIYLHDSEIISH 650
651 GNLRSSNCLIDSRWVCQISDFGLHELKAGQEEPNKSELELKRALCMAPEL 700
701 LRDAYRPGRGSQKGDVYSFGILLYEMIGRKGPWGDTAYSKEEIIQFVKCP 750
751 EMLQHGVFRPALTHTHLDIPDYIRKCLCQCWDEDPEVRPDIRLVRMHLKE 800
801 LQAGLKPNIFDNMLSIMEKYAYNLEGLVQERTNLLYEEKKKTDMLLYQML 850
851 PRPVAELLKRGDPVEAECFDCVTILFSDIVGFTELCTTSTPFEVVEMLND 900
901 WYTCCDSIISNYDVYKVETIGDAYMVVSGLPLQNGSRHAGEIASLALHLL 950
951 ETVGNLKIRHKPTETVQLRIGVHSGPCAAGVVGQKMPRYCLFGDTVNTAS 1000
1001 RMESTGDSMRIHISEATYQLLQVIGSYVCIERGLTSIKGKGDMRTYWLTK 1050
1051 RQQPELTPDLISTVDTLDTYCSGPRESMEVSVHQYCSPASNNYRLGSCNC 1100
1101 DTKCLYSRRSDDNVTNSHGTSEFPKVSEPAQVNCNQLCVCRLNSSQMFNN 1150
1151 RGPRSAPSITFRL 1163
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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