 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9D2F7 from www.uniprot.org...
The NucPred score for your sequence is 0.37 (see score help below)
1 MIAFGAPRRRSFGLLFSLAPHLFFLFLIGTLANKLNVPQVLLPFSREPGR 50
51 VPFLLEAQRGCYIWHSTHHDAVTIQPLYENGTSCSQRAVLVAESTQPIRL 100
101 SSIILAREVVTDHELRCDVKVDVIDNIEIVSRTRELYVDDAPLELMVRAL 150
151 DSKGNTFSTLAGMVFEWSIAQDNESSREELSSKIRILKYSEAEYSPPDYI 200
201 IEMEKQERQGDVILVSGMRTGAAVVKVRIYEPFYKKVAAALIRLLVLENI 250
251 FLIPSHDTYLLVGAYIKYRVAKMVQGRMTEVNFPLEHYTLELQDHRLTNG 300
301 GLPSKSVALLDEKTAMVTAVQLGQTNLVFVHKNVHMRSVSGLPNSTIYVV 350
351 EPGFLGFSIHPGGRWSLEVGQVYVITVEVFDKSSTRVYISDNLKITFQFS 400
401 KEYFEEQLSTSNGSYHVVKALKDGVVVINATLTSSLQERNSSQPKTYQIS 450
451 HQQEVKIYFPIQLKPSFLAFPHHPLGISNRFTVQVEGGSGNFTWSSSNET 500
501 VAMVTTKGVVTAGQVRGNSTILARDVQNPSRSGDIKVYVMKLNKMELLPF 550
551 QADVEIGQIIEVPIAMYHVNTETKEAIAFTDCSHLPLDLNSDKQGVFTLF 600
601 KEGIQKPGAMHCSSVHIAATSPGHTLVTVSVTGHEEHAWSSATFAAYEPL 650
651 KALNPVDVALVTWQSVKEMVFEGGPHPWILEPSRFFLELSMEKAEAIRVA 700
701 EVRLPAKRKQNQYVYRVLCLELGEQVLTFRIGNHPGVLNPSPSVEKVQVR 750
751 FICAHPASMLVTPMYKAPSGTQPCPLPQYNKQLIPVSSLRDSVLELAVFD 800
801 QHGRKFDNFSSLMLEWKSSNETLAHFEDSKSVEMVARDDGSGQTRLHGHQ 850
851 ILKVHQMKGTVLIGVNFAGYSGKKSPKGISNSPRSAGVELILVEDVTVQP 900
901 ENATIYNHPDVKEIFNLVEGSGYFLINSSEQDIVTITYREAESSVQLVPA 950
951 HPGFLTLEVYDLCLAYLGPAVAQIRVSDIQELELDLIDKVEIGKTVLVVV 1000
1001 RVLGSSKHPFRNKYFRNMEVRLQLASAIVTLRLMEDQDEYSENYMLRAVT 1050
1051 VGQTTLVAIATDRMGKKFTSAPRHIEVFPPFRLIPEKMTLIATNMMQIMS 1100
1101 EGGPQPQSIIHFSISNQTVAVVNRRGQVTGKSVGTAVLHGTIQTVNEDTG 1150
1151 KVIVFSQDEVQIEVVQLQAVRILAAATRLVTATEMPVYVMGVTSTQTPFS 1200
1201 FSNASPLLTFHWSMSKRDVLDLVPRHSEVFLQLPAENNFAMVVHTKAAGR 1250
1251 TTIKVTVRSENSSSGQLEGNLLELSDEIQILVFEKLQLFYANCQPEQILM 1300
1301 PMNSQLKLHTNREGAAFVSSRVLKCFPNSSVIEEDGGGLLRSGSIAGTAV 1350
1351 LEVTSIEPFGVNQTTITGVQVAPVTYLRLSSYPKLYTAQGRTLSAFPLGM 1400
1401 SLTFIVEFYNNIGEKFHTHNTRLYMALNRDDLLLIGPGNRNYTYMAQAVN 1450
1451 KGVTVVGLWDQRHPGMADYIPVAVEHAIEPDTKLIFVGDVICFSTQLVNQ 1500
1501 HGEPGVWMISTNNIVQTDTATGVGVARNPGTATIFHNIPGVVKTFREVVV 1550
1551 NASSRLTLSYDLKTYLTNTPNATAFKLFISTGRNVNLKGSCTPSQALAIE 1600
1601 KVLLPETLMLCHVQFSNTLLDIPASKVFHIHSEFSVEKGVYVCLIKVRQE 1650
1651 SKELRQVLSVADTSVYGWATLVSERGKNGMQRILIPFIPGFYMNQSEFVL 1700
1701 GHKDTGELRILGVERVLESLEVFHSSPFLAVSGYKHSMLTTGLTVYLVRI 1750
1751 VNFTAFQQMSSPAFINVSCALTNQQEAVIVRAKDASGADHCEDSGVFKNF 1800
1801 VGSYQILLFTLFAVLASTSFIFLAHNAFLNKVQTIPVVYVPTTGTTQPGS 1850
1851 YTATCSPPHFLSSRPPLVQSRLQHWLWSIKH 1881
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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