 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02357 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MGFCKADAATSFLRAARSGNLDKALDHLRNGVDINTCNQNGLNGLHLASK 50
51 EGHVKMVVELLHKEIILETTTKKGNTALHIAALAGQDEVVRELVNYGANV 100
101 NAQSQKGFTPLYMAAQENHLEVVKFLLENGANQNVATEDGFTPLAVALQQ 150
151 GHENVVAHLINYGTKGKVRLPALHIAARNDDTRTAAVLLQNDPNPDVLSK 200
201 TGFTPLHIAAHYENLNVAQLLLNRGASVNFTPQNGITPLHIASRRGNVIM 250
251 VRLLLDRGAQIETRTKDELTPLHCAARNGHVRISEILLDHGAPIQAKTKN 300
301 GLSPIHMAAQGDHLDCVRLLLQYNAEIDDITLDHLTPLHVAAHCGHHRVA 350
351 KVLLDKGAKPNSRALNGFTPLHIACKKNHIRVMELLLKTGASIDAVTESG 400
401 LTPLHVASFMGHLPIVKNLLQRGASPNVSNVKVETPLHMAARAGHTEVAK 450
451 YLLQNKAKANAKAKDDQTPLHCAARIGHTGMVKLLLENGASPNLATTAGH 500
501 TPLHTAAREGHVDTALALLEKEASQACMTKKGFTPLHVAAKYGKVRLAEL 550
551 LLEHDAHPNAAGKNGLTPLHVAVHHNNLDIVKLLLPRGGSPHSPAWNGYT 600
601 PLHIAAKQNQIEVARSLLQYGGSANAESVQGVTPLHLAAQEGHTEMVALL 650
651 LSKQANGNLGNKSGLTPLHLVSQEGHVPVADVLIKHGVTVDATTRMGYTP 700
701 LHVASHYGNIKLVKFLLQHQADVNAKTKLGYSPLHQAAQQGHTDIVTLLL 750
751 KNGASPNEVSSNGTTPLAIAKRLGYISVTDVLKVVTDETSVVLVSDKHRM 800
801 SYPETVDEILDVSEDEGDELVGSKAERRDSRDVGEEKELLDFVPKLDQVV 850
851 ESPAIPRIPCVTPETVVIRSEDQEQASKEYDEDSLIPSSPATETSDNISP 900
901 VASPVHTGFLVSFMVDARGGSMRGSRHNGLRVVIPPRTCAAPTRITCRLV 950
951 KPQKLNTPPPLAEEEGLASRIIALGPTGAQFLSPVIVEIPHFASHGRGDR 1000
1001 ELVVLRSENGSVWKEHKSRYGESYLDQILNGMDEELGSLEELEKKRVCRI 1050
1051 ITTDFPLYFVIMSRLCQDYDTIGPEGGSLRSKLVPLVQATFPENAVTKKV 1100
1101 KLALQAQPVPDELVTKLLGNQATFSPIVTVEPRRRKFHRPIGLRIPLPPS 1150
1151 WTDNPRDSGEGDTTSLRLLCSVIGGTDQAQWEDITGTTKLIYANECANFT 1200
1201 TNVSARFWLSDCPRTAEAVHFATLLYKELTAVPYMAKFVIFAKMNDAREG 1250
1251 RLRCYCMTDDKVDKTLEQHENFVEVARSRDIEVLEGMPLFAELSGNLVPV 1300
1301 KKAAQQRSFHFQSFRENRLAIPVKVRDSSREPGGFLSFLRKTMKYEDTQH 1350
1351 ILCHLNITMPPCTKGSGAEDRRRTLTPLTLRYSILSESRLGFTSDTDRVE 1400
1401 MRMAVIREHLGLSWAELARELQFSVEDINRIRVENPNSLLDQSTALLTLW 1450
1451 VDREGENAKMENLYTALRNIDRSEIVNMLEGSGRQSRNLKPERRHGDREY 1500
1501 SLSPSQVNGYSSLQDELLSPASLQYALPSPLCADQYWNEVTVIDAIPLAA 1550
1551 TEHDTMLEMSDMQVWSAGLTPSLVTAEDSSLECSKAEDSDAIPEWKLEGA 1600
1601 HSEDTQGPELGSQDLVEDDTVDSDATNGLADLLGQEEGQRSEKKRQEVSG 1650
1651 TEQDTETEVSLVSGQQRVHARITDSPSVRQVLDRSQARTLDWDKQGSTAV 1700
1701 HPQEATQSSWQEEVTQGPHSFQRRITTIQGPEPGALQEYEQVLVSTREHV 1750
1751 QRGPPETGSPKAGKEPSLWAPESAFSQEVQGDELQNIPGEQVTEEQFTDE 1800
1801 QGNIVTKKIIRKVVRQVDSSGAIDTQQHEEVELRGSGLQPDLIEGRKGAQ 1850
1851 IVKRASLKRGKQ 1862
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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