 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P16157 from www.uniprot.org...
The NucPred score for your sequence is 0.60 (see score help below)
1 MPYSVGFREADAATSFLRAARSGNLDKALDHLRNGVDINTCNQNGLNGLH 50
51 LASKEGHVKMVVELLHKEIILETTTKKGNTALHIAALAGQDEVVRELVNY 100
101 GANVNAQSQKGFTPLYMAAQENHLEVVKFLLENGANQNVATEDGFTPLAV 150
151 ALQQGHENVVAHLINYGTKGKVRLPALHIAARNDDTRTAAVLLQNDPNPD 200
201 VLSKTGFTPLHIAAHYENLNVAQLLLNRGASVNFTPQNGITPLHIASRRG 250
251 NVIMVRLLLDRGAQIETKTKDELTPLHCAARNGHVRISEILLDHGAPIQA 300
301 KTKNGLSPIHMAAQGDHLDCVRLLLQYDAEIDDITLDHLTPLHVAAHCGH 350
351 HRVAKVLLDKGAKPNSRALNGFTPLHIACKKNHVRVMELLLKTGASIDAV 400
401 TESGLTPLHVASFMGHLPIVKNLLQRGASPNVSNVKVETPLHMAARAGHT 450
451 EVAKYLLQNKAKVNAKAKDDQTPLHCAARIGHTNMVKLLLENNANPNLAT 500
501 TAGHTPLHIAAREGHVETVLALLEKEASQACMTKKGFTPLHVAAKYGKVR 550
551 VAELLLERDAHPNAAGKNGLTPLHVAVHHNNLDIVKLLLPRGGSPHSPAW 600
601 NGYTPLHIAAKQNQVEVARSLLQYGGSANAESVQGVTPLHLAAQEGHAEM 650
651 VALLLSKQANGNLGNKSGLTPLHLVAQEGHVPVADVLIKHGVMVDATTRM 700
701 GYTPLHVASHYGNIKLVKFLLQHQADVNAKTKLGYSPLHQAAQQGHTDIV 750
751 TLLLKNGASPNEVSSDGTTPLAIAKRLGYISVTDVLKVVTDETSFVLVSD 800
801 KHRMSFPETVDEILDVSEDEGEELISFKAERRDSRDVDEEKELLDFVPKL 850
851 DQVVESPAIPRIPCAMPETVVIRSEEQEQASKEYDEDSLIPSSPATETSD 900
901 NISPVASPVHTGFLVSFMVDARGGSMRGSRHNGLRVVIPPRTCAAPTRIT 950
951 CRLVKPQKLSTPPPLAEEEGLASRIIALGPTGAQFLSPVIVEIPHFASHG 1000
1001 RGDRELVVLRSENGSVWKEHRSRYGESYLDQILNGMDEELGSLEELEKKR 1050
1051 VCRIITTDFPLYFVIMSRLCQDYDTIGPEGGSLKSKLVPLVQATFPENAV 1100
1101 TKRVKLALQAQPVPDELVTKLLGNQATFSPIVTVEPRRRKFHRPIGLRIP 1150
1151 LPPSWTDNPRDSGEGDTTSLRLLCSVIGGTDQAQWEDITGTTKLVYANEC 1200
1201 ANFTTNVSARFWLSDCPRTAEAVNFATLLYKELTAVPYMAKFVIFAKMND 1250
1251 PREGRLRCYCMTDDKVDKTLEQHENFVEVARSRDIEVLEGMSLFAELSGN 1300
1301 LVPVKKAAQQRSFHFQSFRENRLAMPVKVRDSSREPGGSLSFLRKAMKYE 1350
1351 DTQHILCHLNITMPPCAKGSGAEDRRRTPTPLALRYSILSESTPGSLSGT 1400
1401 EQAEMKMAVISEHLGLSWAELARELQFSVEDINRIRVENPNSLLEQSVAL 1450
1451 LNLWVIREGQNANMENLYTALQSIDRGEIVNMLEGSGRQSRNLKPDRRHT 1500
1501 DRDYSLSPSQMNGYSSLQDELLSPASLGCALSSPLRADQYWNEVAVLDAI 1550
1551 PLAATEHDTMLEMSDMQVWSAGLTPSLVTAEDSSLECSKAEDSDATGHEW 1600
1601 KLEGALSEEPRGPELGSLELVEDDTVDSDATNGLIDLLEQEEGQRSEEKL 1650
1651 PGSKRQDDATGAGQDSENEVSLVSGHQRGQARITHSPTVSQVTERSQDRL 1700
1701 QDWDADGSIVSYLQDAAQGSWQEEVTQGPHSFQGTSTMTEGLEPGGSQEY 1750
1751 EKVLVSVSEHTWTEQPEAESSQADRDRRQQGQEEQVQEAKNTFTQVVQGN 1800
1801 EFQNIPGEQVTEEQFTDEQGNIVTKKIIRKVVRQIDLSSADAAQEHEEVT 1850
1851 VEGPLEDPSELEVDIDYFMKHSKDHTSTPNP 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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