 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02952 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MGAGSSTEQRSPEQPPEGSSTPAEPEPSGGGPSAEAAPDTTADPAIAASD 50
51 PATKLLQKNGQLSTINGVAEQDELSLQEGDLNGQKGALNGQGALNSQEEE 100
101 EVIVTEVGQRDSEDVSKRDSDKEMATKSAVVHDITDDGQEETPEIIEQIP 150
151 SSESNLEELTQPTESQANDIGFKKVFKFVGFKFTVKKDKTEKPDTVQLLT 200
201 VKKDEGEGAAGAGDHKDPSLGAGEAASKESEPKQSTEKPEETLKREQSHA 250
251 EISPPAESGQAVEECKEEGEEKQEKEPSKSAESPTSPVTSETGSTFKKFF 300
301 TQGWAGWRKKTSFRKPKEDEVEASEKKKEQEPEKVDTEEDGKAEVASEKL 350
351 TASEQAHPQEPAESAHEPRLSAEYEKVELPSEEQVSGSQGPSEEKPAPLA 400
401 TEVFDEKIEVHQEEVVAEVHVSTVEERTEEQKTEVEETAGSVPAEELVEM 450
451 DAEPQEAEPAKELVKLKETCVSGEDPTQGADLSPDEKVLSKPPEGVVSEV 500
501 EMLSSQERMKVQGSPLKKLFTSTGLKKLSGKKQKGKRGGGDEESGEHTQV 550
551 PADSPDSQEEQKGESSASSPEEPEEITCLEKGLAEVQQDGEAEEGATSDG 600
601 EKKREGVTPWASFKKMVTPKKRVRRPSESDKEDELDKVKSATLSSTESTA 650
651 SEMQEEMKGSVEEPKPEEPKRKVDTSVSWEALICVGSSKKRARRGSSSDE 700
701 EGGPKAMGGDHQKADEAGKDKETGTDGILAGSQEHDPGQGSSSPEQAGSP 750
751 TEGEGVSTWESFKRLVTPRKKSKSKLEEKSEDSIAGSGVEHSTPDTEPGK 800
801 EESWVSIKKFIPGRRKKRPDGKQEQAPVEDAGPTGANEDDSDVPAVVPLS 850
851 EYDAVEREKMEAQQAQKSAEQPEQKAATEVSKELSESQVHMMAAAVADGT 900
901 RAATIIEERSPSWISASVTEPLEQVEAEAALLTEEVLEREVIAEEEPPTV 950
951 TEPLPENREARGDTVVSEAELTPEAVTAAETAGPLGAEEGTEASAAEETT 1000
1001 EMVSAVSQLTDSPDTTEEATPVQEVEGGVPDIEEQERRTQEVLQAVAEKV 1050
1051 KEESQLPGTGGPEDVLQPVQRAEAERPEEQAEASGLKKETDVVLKVDAQE 1100
1101 AKTEPFTQGKVVGQTTPESFEKAPQVTESIESSELVTTCQAETLAGVKSQ 1150
1151 EMVMEQAIPPDSVETPTDSETDGSTPVADFDAPGTTQKDEIVEIHEENEV 1200
1201 ASGTQSGGTEAEAVPAQKERPPAPSSFVFQEETKEQSKMEDTLEHTDKEV 1250
1251 SVETVSILSKTEGTQEADQYADEKTKDVPFFEGLEGSIDTGITVSREKVT 1300
1301 EVALKGEGTEEAECKKDDALELQSHAKSPPSPVEREMVVQVEREKTEAEP 1350
1351 THVNEEKLEHETAVTVSEEVSKQLLQTVNVPIIDGAKEVSSLEGSPPPCL 1400
1401 GQEEAVCTKIQVQSSEASFTLTAAAEEEKVLGETANILETGETLEPAGAH 1450
1451 LVLEEKSSEKNEDFAAHPGEDAVPTGPDCQAKSTPVIVSATTKKGLSSDL 1500
1501 EGEKTTSLKWKSDEVDEQVACQEVKVSVAIEDLEPENGILELETKSSKLV 1550
1551 QNIIQTAVDQFVRTEETATEMLTSELQTQAHVIKADSQDAGQETEKEGEE 1600
1601 PQASAQDETPITSAKEESESTAVGQAHSDISKDMSEASEKTMTVEVEGST 1650
1651 VNDQQLEEVVLPSEEEGGGAGTKSVPEDDGHALLAERIEKSLVEPKEDEK 1700
1701 GDDVDDPENQNSALADTDASGGLTKESPDTNGPKQKEKEDAQEVELQEGK 1750
1751 VHSESDKAITPQAQEELQKQERESAKSELTES 1782
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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