 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q03343 from www.uniprot.org...
The NucPred score for your sequence is 0.32 (see score help below)
1 MSWFSGLLVPKVDERKTAWGERNGQKRPRQATRARGFCAPRYMSCLKNVE 50
51 PPSPTPAARTRCPWQDEAFIRRAGPGRGVKLGLRSVALGFDDTEVTTPMG 100
101 TAEVAPDTSPRSGPSCWHRLAQVFQSKQFRSAKLERLYQRYFFQMNQSSL 150
151 TLLMAVLVLLMAVLLTFHAAPALPQPAYVALLTCASVLFVVLMVVCNRHS 200
201 FRQDSMWVVSYVVLGILAAVQVGGALAANPRSPSAGLWCPVFFVYITYTL 250
251 LPIRMRAAVLSGLGLSTLHLILAWHLNNGDPFLWKQLGANVVLFLCTNAI 300
301 GVCTHYPAEVSQRQAFQETRGYIQARLHLQHENRQQERLLLSVLPQHVAM 350
351 EMKEDINTKKEDMMFHKIYIQKHDNVSILFADIEGFTSLASQCTAQELVM 400
401 TLNELFARFDKLAAENHCLRIKILGDCYYCVSGLPEARADHAHCCVEMGV 450
451 DMIEAISLVREVTGVNVNMRVGIHSGRVHCGVLGLRKWQFDVWSNDVTLA 500
501 NHMEAGGRAGRIHITRATLQYLNGDYEVEPGRGGERNGYLKEQCIETFLI 550
551 LGASQKRKEEKAMLVKLQRTRANSMEGLMPRWVPDRAFSRTKDSKAFRQM 600
601 GIDDSSKENRGAQDALNPEDEVDEFLGRAIDARSIDQLRKDHVRRFLLTF 650
651 QREDLEKKYSRKVDPRFGAYVACALLVFCFICFIQFLVFPHSALILGIYA 700
701 GIFLLLLVTVLICAVCSCGSFFPNALQRLSRSIVRSRVHSTAVGVFSVLL 750
751 VFISAIANMFTCSHTPLRTCAARMLNLTPSDVTACHLRQINYSLGLEAPL 800
801 CEGTAPTCSFPEYFVGSVLLSLLASSVFLHISSIGKLVMTFVLGFIYLLL 850
851 LLLGPPATIFDNYDLLLSVHGLASSNETFDGLDCPAVGRVALKYMTPVIL 900
901 LVFALALYLHAQQVESTARLDFLWKLQATGEKEEMEELQAYNRRLLHNIL 950
951 PKDVAAHFLARERRNDELYYQSCECVAVMFASIANFSEFYVELEANNEGV 1000
1001 ECLRLLNEIIADFDEIISEERFRQLEKIKTIGSTYMAASGLNASTYDQVG 1050
1051 RSHITALADYAMRLMEQMKHINEHSFNNFQMKIGLNMGPVVAGVIGARKP 1100
1101 QYDIWGNTVNVSSRMDSTGVPDRIQVTTDLYQVLAAKGYQLECRGVVKVK 1150
1151 GKGEMTTYFLNGGPSS 1166
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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