 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8CGU4 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MSRGAGALQRRTTTYLISLTLVKLESVPPPPPSPSAAAVGAPGARGSEPR 50
51 DPGSPRGAEEPGKKRHERLFHRQDALWISTSSAGAGGAEPPALSPAPASP 100
101 ARPVSPAPGRRLSLWAAPPGPPLSGGLSPDSKPGGAPSSSRRPLLSSPSW 150
151 GGPEPEGRTGGGVPGSSSPHPGTGSRRLKVAPPPPAPKPFKTVTTSGAKA 200
201 GGGKGAGSRLSWPESEGKPRVKGSKSTAGTGASAVAAGGGGSAAVTTSGG 250
251 VGAGAGARGKLSPRKGKSKTLDNSDLHPGPSAGSPPLTVPAIPVPATSVT 300
301 AASTQPLGPAPPITLEPPAPGLKRGREGGRASTRDRKMLKFISGIFTKST 350
351 GGPPGPGPLPGPQGLSSSSGSRELLGAELRASPKAVVNSQEWTLSRSIPE 400
401 LRLGVLGDVRSGKSSLIHRFLTGSYQVLEKPESEQYKKEMLVDGQTHLVL 450
451 IREEAGAPDAKFSGWADAVIFVFSLEDESSFQAVSHLHGQLISLRGEGRG 500
501 GLALALVGTQDRISASSPRVVGDARARALCTDMKRCSYYETCATYGLNVD 550
551 RVFQEVAQKVVTLRKQQQLLAACKSLPSSPSHSAASTPVAGQASNGGHTS 600
601 DYSSSLPSSPNVGHRELRAEAAAVAGLSTPGSLHRAAKRRTSLFANRRGS 650
651 DSEKRSLDSRGETTGSGRAIPIKQSFLLKRSGNSLNKEWKKKYVTLSSNG 700
701 FLLYHPSINDYIHSTHGKEMDLLRTTVKVPGKRPPRAISAFGPSASINGL 750
751 VKDMSTVQMGEGPEASTPMPSPSPSPSSLQLPTDQTSKHLLKPDRNLARA 800
801 LSTDCTPSGDLSPLSREPPPSPMVKKQRRKKLSTPSKTEGSAVQAEAKRK 850
851 MWKLKSFGSLRNIYKAEENFEFLIVSSTGQTWHFEAASFEERDAWVQAIE 900
901 SQILASLQCCESSKVKLRTDSQSEAVAIQAIRNAKGNSTCVDCGAPNPTW 950
951 ASLNLGALICIECSGIHRNLGTHLSRVRSLDLDDWPRELTLVLTAIGNDT 1000
1001 ANRVWESDTRGRAKPTRDSSREERESWIRAKYEQLLFLAPLGTTEEPLGR 1050
1051 QLWAAVEAQDVAAVLLLLAHARHGPLDTSVEDPQLRSPLHLAAELAHVVI 1100
1101 TQLLLWYGADVAARDAQGRTALFYARQAGSQLCADILLQHGCPGEGGSTA 1150
1151 TTPSAATTPSITATPSPRRRSSAASLGRVDTTIALV 1186
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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