 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q09904 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MPPVSKNTRTSSKTVKKPYDPPQGSSRPFFTVLKRAFSSVLHPFTSGLDE 50
51 KASGTASKDRKSGRAGTKSLLTPELTPHYLGKSPRIIRVSNRSHVRTIDG 100
101 IEEKVHTNTFEPRKPKQKQDYTNSPTLFKRHDELSLKSLNSLHPSSALSK 150
151 KLGSTSQHQIATPKSSASLLNILRSLHDEQKNTLNISSVKQDRITEANPT 200
201 CEKRKPSRSPSPMLSKKKSVARASENEPSAKQNKSFSGNDSHKSLTDIRD 250
251 KENGETEVSAKNHVPHRSSRRRRRHQRLIPIIYETLEQMDLRKPVLVNAE 300
301 VQTDSNPGNTMFIDKQDIYHRLSTPTSRKRQTLEKGHIKAFSAVDEDLDE 350
351 IFACEDDVHYTALPKQNPKSERILEPIIASPKDNTSDKGLLTKSAPTFEE 400
401 LQASITPKPVKTSPNDTALTLANAEDNKTFEHQPLSKDTEAPKSQFSSSP 450
451 TKESTTRKSEVEPPSPSKEIKSSHFSVPEFKFEPKTEATTDKKLNVPKFE 500
501 FKPTATADVQTNRLKENEPKPTFFAQLPSKTQETPSITENKPSFFSQLSP 550
551 KREETEKKDNAPSAPASTSGFSFGGFAPKTLEEKEETKAPTFNFSLNNAS 600
601 STQDTTKPTLQFNFGSSFGKPTSNIFNDKKTSENGLASSTVASESKPSAP 650
651 ESKPSSGFGNTAGSSPFSFNLTKESKEVPPTNSFSFAKKGKDEANDSLSA 700
701 KASTPFSFAKPNTENVTTTAPQFSFNFTKPNTDAKTNLLPEKTFNEEAVK 750
751 QKETEKEVPPTGPKASEIKDSVSSNNAVPSSTFNFVSPFAAVSEKTNENN 800
801 IPNDTTKTNGNATKRTLEQTEDAKPFAFSFGSTTEQANKKASTSNETTKP 850
851 QLDTSSKTDGVTANAPFSFASAFNAPKPSTNTADGKDSASNLTTPSPAFS 900
901 FGNNSGVKASSNNNPSTNSSTAPFSFGTSNKPAFSFGSATSKTTSEGTAP 950
951 AASASAPAPTTSAFSFGASNSSMNKEENTPMAKDAGDTAPASGFKSGFSF 1000
1001 GANNSPQPASMFGTSTPAPSSAFAFGNQSGTNPAAPAGFGGITNTATNNP 1050
1051 PSTGFTFTPSNAGSTAAPMFGAGNTPNPSGSINNASQAFAFGSGEPSNPA 1100
1101 SNPPSTGFSFGAATPSAFNASASQSPAPNGIQFNLGSSNSQTNAPPGRKI 1150
1151 AVPRSRRKR 1159
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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