| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9VJJ7 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MMEEENTIRPHQEIRQLTLEEKKFLLAVERGDMAGTRRMLQKAQDTEYIN 50
51 VNCVDPLGRTALLMAIDNENLEMVELLINYNVDTKDALLHSISEEFVEAV 100
101 EVLLDHENVTFHSEGNHSWESASEDTSTFTPDITPLILAAHRDNYEIIKI 150
151 LLDRGAVLPMPHDVRCGCDECVQSRQEDSLRHSRSRINAYRALASPSLIA 200
201 LSSKDPILTAFELSWELRRLSFLEHEFKNEYQELRKQCQDFATALLDHTR 250
251 TSHELEILLNHDPTGPVYEHGERMHLNRLKLAIKLRQKKFVAHSNVQQLL 300
301 ASIWYEGLPGFRRKNMALQAVDIIRIGIMFPIFSLAYILAPYSSIGQTMR 350
351 KPFIKFICHSASYFTFLFLLMLASQRIETFIGGWFFADSSGMLNTMEELP 400
401 TKRGAKPTFIEWLILAWVSGLIWSEVKQLWDVGLQEYLNDMWNVIDFVTN 450
451 SLYVATVALRVVSFFQVQKEMIYNSHATDLPRERWDAWDPMLISEGLFSA 500
501 ANIFSSLKLVYIFSVNPHLGPLQVSLSRMVMDIMKFFFLYVLVLFAFGSG 550
551 LNQLLWYYADLEKKRCPEVSPMSALLNMNGTNDPNACIVWRRFSNLFETT 600
601 QTLFWAVFGLIDLDSFELDGIKIFTRFWGMLMFGTYSVINIVVLLNLLIA 650
651 MMNHSYQLISERADVEWKFARSKLWISYFEEGGTCPPPFNIIPTPKSIWY 700
701 AIKWMRRVFCSGSSAARREHLKTIRRKAQQASDRDFKYQQIMRNLVRRYV 750
751 TVEQRKAESQGVTEDDVNEIKQDISAFRCELVEILKNSGMDTNVTAGQGG 800
801 GGGGKKNRQKERRLMKGFNIAPPGSTGSLAPVAEFSTSLDNYDNQHEILS 850
851 STLSTLFTPNFMHKRQQSQAGSGGGGSESPTTPTAPQGTQGAAMTASSQV 900
901 TKYNKSALKPYNKRIAGHKKRWGTLIEAAKVGNVSKMLGRSKSEDSVCNS 950
951 SHTSTPVHGQMRVTYAQNSPQQEYGYHGETSSTTISTPTPTISVVSNSPA 1000
1001 AHAGVGSHFFHTTSGLTAIAALKRKRKKFSSSKNICPVTESVAAANAAEI 1050
1051 LNDKTLKRVSSYPAAEAGVQHNPAQLVKPRRHEQTQSQHDSVETNSTFTL 1100
1101 SIDPSNTSVNSREPLISTSCVSTTGAIG 1128
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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