 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Y6D5 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MQESQTKSMFVSRALEKILADKEVKRPQHSQLRRACQVALDEIKAEIEKQ 50
51 RLGTAAPPKANFIEADKYFLPFELACQSKSPRVVSTSLDCLQKLIAYGHI 100
101 TGNAPDSGAPGKRLIDRIVETICSCFQGPQTDEGVQLQIIKALLTAVTSP 150
151 HIEIHEGTILQTVRTCYNIYLASKNLINQTTAKATLTQMLNVIFTRMENQ 200
201 VLQEARELEKPIQSKPQSPVIQAAAVSPKFVRLKHSQAQSKPTTPEKTDL 250
251 TNGEHARSDSGKVSTENGDAPRERGSSLSGTDDGAQEVVKDILEDVVTSA 300
301 IKEAAEKHGLTEPERVLGELECQECAIPPGVDENSQTNGIADDRQSLSSA 350
351 DNLESDAQGHQVAARFSHVLQKDAFLVFRSLCKLSMKPLGEGPPDPKSHE 400
401 LRSKVVSLQLLLSVLQNAGPVFRTHEMFINAIKQYLCVALSKNGVSSVPD 450
451 VFELSLAIFLTLLSNFKMHLKMQIEVFFKEIFLNILETSTSSFEHRWMVI 500
501 QTLTRICADAQCVVDIYVNYDCDLNAANIFERLVNDLSKIAQGRSGHELG 550
551 MTPLQELSLRKKGLECLVSILKCMVEWSKDLYVNPNHQTSLGQERLTDQE 600
601 IGDGKGLDMARRCSVTSMESTVSSGTQTTVQDDPEQFEVIKQQKEIIEHG 650
651 IELFNKKPKRGIQFLQEQGMLGTSVEDIAQFLHQEERLDSTQVGDFLGDS 700
701 ARFNKEVMYAYVDQLDFCEKEFVSALRTFLEGFRLPGEAQKIDRLMEKFA 750
751 ARYIECNQGQTLFASADTAYVLAYSIIMLTTDLHSPQVKNKMTKEQYIKM 800
801 NRGINDSKDLPEEYLSSIYEEIEGKKIAMKETKELTIATKSTKQNVASEK 850
851 QRRLLYNLEMEQMAKTAKALMEAVSHAKAPFTSATHLDHVRPMFKLVWTP 900
901 LLAAYSIGLQNCDDTEVASLCLEGIRCAIRIACIFGMQLERDAYVQALAR 950
951 FSLLTASSSITEMKQKNIDTIKTLITVAHTDGNYLGNSWHEILKCISQLE 1000
1001 LAQLIGTGVKTRYLSGSGREREGSLKGHTLAGEEFMGLGLGNLVSGGVDK 1050
1051 RQMASFQESVGETSSQSVVVAVDRIFTGSTRLDGNAIVDFVRWLCAVSMD 1100
1101 ELASPHHPRMFSLQKIVEISYYNMNRIRLQWSRIWHVIGDHFNKVGCNPN 1150
1151 EDVAIFAVDSLRQLSMKFLEKGELANFRFQKDFLRPFEHIMKKNRSPTIR 1200
1201 DMAIRCIAQMVNSQAANIRSGWKNIFAVFHQAASDHDGNIVELAFQTTCH 1250
1251 IVTTIFQHHFPAAIDSFQDAVKCLSEFACNAAFPDTSMEAIRLIRFCGKY 1300
1301 VSERPRVLQEYTSDDMNVAPGDRVWVRGWFPILFELSCIINRCKLDVRTR 1350
1351 GLTVMFEIMKSYGHTFEKHWWQDLFRIVFRIFDNMKLPEQLSEKSEWMTT 1400
1401 TCNHALYAICDVFTQFYEALNEVLLSDVFAQLQWCVKQDNEQLARSGTNC 1450
1451 LENLVISNGEKFSPEVWDETCNCMLDIFKTTIPHVLLTWRPVGMEEDSSE 1500
1501 KHLDVDLDRQSLSSIDKNPSERGQSQLSNPTDDSWKGRPYANQKLFASLL 1550
1551 IKCVVQLELIQTIDNIVFYPATSKKEDAEHMVAAQQDTLDADIHIETEDQ 1600
1601 GMYKYMSSQHLFKLLDCLQESHSFSKAFNSNYEQRTVLWRAGFKGKSKPN 1650
1651 LLKQETSSLACCLRILFRMYVDENRRDSWEEIQQRLLTVCSEALAYFITV 1700
1701 NSESHREAWTSLLLLLLTKTLKINDEKFKAHASMYYPYLCEIMQFDLIPE 1750
1751 LRAVLRKFFLRIGVVYKIWIPEEPSQVPAALSPVW 1785
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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