 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6PDQ2 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MASGLGSPSPCSAGSEEEDMDALLNNSLPPPHPENEEDPDEDLSEAETPK 50
51 LKKKKKPKKPRDPKIPKSKRQKKELGDSSGEGPEFVEEEEEVALRSDSEG 100
101 SDYTPGKKKKKKLGPKKEKKSKSKRKEEEEEEDEDDDSKEPKSSAQLLED 150
151 WGMEDIDHVFSEEDYRTLTNYKAFSQFVRPLIAAKNPKIAVSKMMMVLGA 200
201 KWREFSTNNPFKGSSGASVAAAAAAAVAVVESMVTATEVAPPPPPVEVPI 250
251 RKAKTKEGKGPNARRKPKGSPRVPDAKKPKPKKVAPLKIKLGGFGSKRKR 300
301 SSSEDDDLDVESDFDDASINSYSVSDGSTSRSSRSRKKLRTAKKKKKGEE 350
351 EVTAVDGYETDHQDYCEVCQQGGEIILCDTCPRAYHMVCLDPDMEKAPEG 400
401 KWSCPHCEKEGIQWEAKEDNSEGEEILEEVGGDPEEEDDHHMEFCRVCKD 450
451 GGELLCCDTCPSSYHIHCLNPPLPEIPNGEWLCPRCTCPALKGKVQKILI 500
501 WKWGQPPSPTPVPRPPDADPNTPSPKPLEGRPERQFFVKWQGMSYWHCSW 550
551 VSELQLELHCQVMFRNYQRKNDMDEPPSGDFGGDEEKSRKRKNKDPKFAE 600
601 MEERFYRYGIKPEWMMIHRILNHSVDKKGHVHYLIKWRDLPYDQASWESE 650
651 DVEIQDYDLFKQSYWNHRELMRGEEGRPGKKLKKVKLRKLERPPETPTVD 700
701 PTVKYERQPEYLDATGGTLHPYQMEGLNWLRFSWAQGTDTILADEMGLGK 750
751 TVQTAVFLYSLYKEGHSKGPFLVSAPLSTIINWEREFEMWAPDMYVVTYV 800
801 GDKDSRAIIRENEFSFEDNAIRGGKKASRMKKEASVKFHVLLTSYELITI 850
851 DMAILGSIDWACLIVDEAHRLKNNQSKFFRVLNGYSLQHKLLLTGTPLQN 900
901 NLEELFHLLNFLTPERFHNLEGFLEEFADIAKEDQIKKLHDMLGPHMLRR 950
951 LKADVFKNMPSKTELIVRVELSPMQKKYYKYILTRNFEALNARGGGNQVS 1000
1001 LLNVVMDLKKCCNHPYLFPVAAMEAPKMPNGMYDGSALIRASGKLLLLQK 1050
1051 MLKNLKEGGHRVLIFSQMTKMLDLLEDFLEHEGYKYERIDGGITGNMRQE 1100
1101 AIDRFNAPGAQQFCFLLSTRAGGLGINLATADTVIIYDSDWNPHNDIQAF 1150
1151 SRAHRIGQNKKVMIYRFVTRASVEERITQVAKKKMMLTHLVVRPGLGSKT 1200
1201 GSMSKQELDDILKFGTEELFKDEATDGGGDNKEGEDSSVIHYDDKAIERL 1250
1251 LDRNQDETEDTELQGMNEYLSSFKVAQYVVREEEMGEEEEVEREIIKQEE 1300
1301 SVDPDYWEKLLRHHYEQQQEDLARNLGKGKRIRKQVNYNDGSQEDRDWQD 1350
1351 DQSDNQSDYSVASEEGDEDFDERSEAPRRPSRKGLRNDKDKPLPPLLARV 1400
1401 GGNIEVLGFNARQRKAFLNAIMRYGMPPQDAFTTQWLVRDLRGKSEKEFK 1450
1451 AYVSLFMRHLCEPGADGAETFADGVPREGLSRQHVLTRIGVMSLIRKKVQ 1500
1501 EFEHVNGRWSMPELAEVEENKKMSQPGSPSPKTPTPSTPGDTQPNTPAPV 1550
1551 PPAEDGIKIEENSLKEEESTEGEKEVKSTAPEATVECAQPPAPAPATAPA 1600
1601 TATAPEDDKAPAEPPEGEEKVEKAEVKERTEEPMETEAKGTTEVEKVEEK 1650
1651 SAVDLTPIVVEDKEEKKEEEEKKDVMLQNGETPKDLSDEKQKKNSKQRFM 1700
1701 FNIADGGFTELHSLWQNEERAATVTKKTYEIWHRRHDYWLLAGIINHGYA 1750
1751 RWQDIQNDPRYAILNEPFKGEMNRGNFLEIKNKFLARRFKLLEQALVIEE 1800
1801 QLRRAAYLNMSEDPSHPSMALNTRFAEVECLAESHQHLSKESMAGNKPAN 1850
1851 AVLHKVLKQLEELLSDMKADVTRLPATIARIPPVAVRLQMSERNILSRLA 1900
1901 NRAPEPPPQQVAQQQ 1915
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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