SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q8VDD5 from www.uniprot.org...

The NucPred score for your sequence is 0.91 (see score help below)

   1  MAQQAADKYLYVDKNFINNPLAQADWAAKKLVWVPSSKNGFEPASLKEEV    50
51 GEEAIVELVENGKKVKVNKDDIQKMNPPKFSKVEDMAELTCLNEASVLHN 100
101 LKERYYSGLIYTYSGLFCVVINPYKNLPIYSEEIVEMYKGKKRHEMPPHI 150
151 YAITDTAYRSMMQDREDQSILCTGESGAGKTENTKKVIQYLAHVASSHKS 200
201 KKDQGELERQLLQANPILEAFGNAKTVKNDNSSRFGKFIRINFDVNGYIV 250
251 GANIETYLLEKSRAIRQAKEERTFHIFYYLLSGAGEHLKTDLLLEPYNKY 300
301 RFLSNGHVTIPGQQDKDMFQETMEAMRIMGIPEDEQMGLLRVISGVLQLG 350
351 NIAFKKERNTDQASMPDNTAAQKVSHLLGINVTDFTRGILTPRIKVGRDY 400
401 VQKAQTKEQADFAIEALAKATYERMFRWLVLRINKALDKTKRQGASFIGI 450
451 LDIAGFEIFDLNSFEQLCINYTNEKLQQLFNHTMFILEQEEYQREGIEWN 500
501 FIDFGLDLQPCIDLIEKPAGPPGILALLDEECWFPKATDKSFVEKVVQEQ 550
551 GTHPKFQKPKQLKDKADFCIIHYAGKVDYKADEWLMKNMDPLNDNIATLL 600
601 HQSSDKFVSELWKDVDRIIGLDQVAGMSETALPGAFKTRKGMFRTVGQLY 650
651 KEQLAKLMATLRNTNPNFVRCIIPNHEKKAGKLDPHLVLDQLRCNGVLEG 700
701 IRICRQGFPNRVVFQEFRQRYEILTPNSIPKGFMDGKQACVLMIKALELD 750
751 SNLYRIGQSKVFFRAGVLAHLEEERDLKITDVIIGFQACCRGYLARKAFA 800
801 KRQQQLTAMKVLQRNCAAYLRLRNWQWWRLFTKVKPLLNSIRHEDELLAK 850
851 EAELTKVREKHLAAENRLTEMETMQSQLMAEKLQLQEQLQAETELCAEAE 900
901 ELRARLTAKKQELEEICHDLEARVEEEEERCQYLQAEKKKMQQNIQELEE 950
951 QLEEEESARQKLQLEKVTTEAKLKKLEEDQIIMEDQNCKLAKEKKLLEDR 1000
1001 VAEFTTNLMEEEEKSKSLAKLKNKHEAMITDLEERLRREEKQRQELEKTR 1050
1051 RKLEGDSTDLSDQIAELQAQIAELKMQLAKKEEELQAALARVEEEAAQKN 1100
1101 MALKKIRELETQISELQEDLESERASRNKAEKQKRDLGEELEALKTELED 1150
1151 TLDSTAAQQELRSKREQEVSILKKTLEDEAKTHEAQIQEMRQKHSQAVEE 1200
1201 LADQLEQTKRVKATLEKAKQTLENERGELANEVKALLQGKGDSEHKRKKV 1250
1251 EAQLQELQVKFSEGERVRTELADKVTKLQVELDSVTGLLSQSDSKSSKLT 1300
1301 KDFSALESQLQDTQELLQEENRQKLSLSTKLKQMEDEKNSFREQLEEEEE 1350
1351 AKRNLEKQIATLHAQVTDMKKKMEDGVGCLETAEEAKRRLQKDLEGLSQR 1400
1401 LEEKVAAYDKLEKTKTRLQQELDDLLVDLDHQRQSVSNLEKKQKKFDQLL 1450
1451 AEEKTISAKYAEERDRAEAEAREKETKALSLARALEEAMEQKAELERLNK 1500
1501 QFRTEMEDLMSSKDDVGKSVHELEKSKRALEQQVEEMKTQLEELEDELQA 1550
1551 TEDAKLRLEVNLQAMKAQFERDLQGRDEQSEEKKKQLVRQVREMEAELED 1600
1601 ERKQRSMAMAARKKLEMDLKDLEAHIDTANKNREEAIKQLRKLQAQMKDC 1650
1651 MRELDDTRASREEILAQAKENEKKLKSMEAEMIQLQEELAAAERAKRQAQ 1700
1701 QERDELADEIANSSGKGALALEEKRRLEARIAQLEEELEEEQGNTELIND 1750
1751 RLKKANLQIDQINTDLNLERSHAQKNENARQQLERQNKELKAKLQEMESA 1800
1801 VKSKYKASIAALEAKIAQLEEQLDNETKERQAASKQVRRTEKKLKDVLLQ 1850
1851 VEDERRNAEQFKDQADKASTRLKQLKRQLEEAEEEAQRANASRRKLQREL 1900
1901 EDATETADAMNREVSSLKNKLRRGDLPFVVTRRIVRKGTGDCSDEEVDGK 1950
1951 ADGADAKAAE 1960

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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