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

NucPred

Fetching Q01484 from www.uniprot.org...

The NucPred score for your sequence is 0.95 (see score help below)

   1  MMNEDAAQKSDSGEKFNGSSQRRKRPKKSDSNASFLRAARAGNLDKVVEY    50
  51  LKGGIDINTCNQNGLNALHLAAKEGHVGLVQELLGRGSSVDSATKKGNTA   100
 101  LHIASLAGQAEVVKVLVKEGANINAQSQNGFTPLYMAAQENHIDVVKYLL   150
 151  ENGANQSTATEDGFTPLAVALQQGHNQAVAILLENDTKGKVRLPALHIAA   200
 201  RKDDTKSAALLLQNDHNADVQSKMMVNRTTESGFTPLHIAAHYGNVNVAT   250
 251  LLLNRGAAVDFTARNGITPLHVASKRGNTNMVKLLLDRGGQIDAKTRDGL   300
 301  TPLHCAARSGHDQVVELLLERGAPLLARTKNGLSPLHMAAQGDHVECVKH   350
 351  LLQHKAPVDDVTLDYLTALHVAAHCGHYRVTKLLLDKRANPNARALNGFT   400
 401  PLHIACKKNRIKVMELLVKYGASIQAITESGLTPIHVAAFMGHLNIVLLL   450
 451  LQNGASPDVTNIRGETALHMAARAGQVEVVRCLLRNGALVDARAREEQTP   500
 501  LHIASRLGKTEIVQLLLQHMAHPDAATTNGYTPLHISAREGQVDVASVLL   550
 551  EAGAAHSLATKKGFTPLHVAAKYGSLDVAKLLLQRRAAADSAGKNGLTPL   600
 601  HVAAHYDNQKVALLLLEKGASPHATAKNGYTPLHIAAKKNQMQIASTLLN   650
 651  YGAETNIVTKQGVTPLHLASQEGHTDMVTLLLDKGANIHMSTKSGLTSLH   700
 701  LAAQEDKVNVADILTKHGADQDAHTKLGYTPLIVACHYGNVKMVNFLLKQ   750
 751  GANVNAKTKNGYTPLHQAAQQGHTHIINVLLQHGAKPNATTANGNTALAI   800
 801  AKRLGYISVVDTLKVVTEEVTTTTTTITEKHKLNVPETMTEVLDVSDEEG   850
 851  DDTMTGDGGEYLRPEDLKELGDDSLPSSQFLDGMNYLRYSLEGGRSDSLR   900
 901  SFSSDRSHTLSHASYLRDSAVMDDSVVIPSHQVSTLAKEAERNSYRLSWG   950
 951  TENLDNVALSSSPIHSGFLVSFMVDARGGAMRGCRHNGLRIIIPPRKCTA  1000
1001  PTRVTCRLVKRHRLATMPPMVEGEGLASRLIEVGPSGAQFLGKLHLPTAP  1050
1051  PPLNEGESLVSRILQLGPPGTKFLGPVIVEIPHFAALRGKERELVVLRSE  1100
1101  NGDSWKEHFCDYTEDELNEILNGMDEVLDSPEDLEKKRICRIITRDFPQY  1150
1151  FAVVSRIKQDSNLIGPEGGVLSSTVVPQVQAVFPEGALTKRIRVGLQAQP  1200
1201  MHSELVKKILGNKATFSPIVTLEPRRRKFHKPITMTIPVPKASSDVMLNG  1250
1251  FGGDAPTLRLLCSITGGTTPAQWEDITGTTPLTFVNECVSFTTNVSARFW  1300
1301  LIDCRQIQESVTFASQVYREIICVPYMAKFVVFAKSHDPIEARLRCFCMT  1350
1351  DDKVDKTLEQQENFAEVARSRDVEVLEGKPIYVDCFGNLVPLTKSGQHHI  1400
1401  FSFFAFKENRLPLFVKVRDTTQEPCGRLSFMKEPKSTRGLVHQAICNLNI  1450
1451  TLPIYTKESESDQEQEEEIDMTSEKNDETESTETSVLKSHLVNEVPVLAS  1500
1501  PDLLSEVSEMKQDLIKMTAILTTDVSDKAGSIKVKELVKAAEEEPGEPFE  1550
1551  IVERVKEDLEKVNEILRSGTCTRDESSVQSSRSERGLVEEEWVIVSDEEI  1600
1601  EEARQKAPLEITEYPCVEVRIDKEIKGKVEKDSTGLVNYLTDDLNTCVPL  1650
1651  PKEQLQTVQDKAGKKCEALAVGRSSEKEGKDIPPDETQSTQKQHKPSLGI  1700
1701  KKPVRRKLKEKQKQKEEGLQASAEKAELKKGSSEESLGEDPGLAPEPLPT  1750
1751  VKATSPLIEETPIGSIKDKVKALQKRVEDEQKGRSKLPIRVKGKEDVPKK  1800
1801  TTHRPHPAASPSLKSERHAPGSPSPKTERHSTLSSSAKTERHPPVSPSSK  1850
1851  TEKHSPVSPSAKTERHSPASSSSKTEKHSPVSPSTKTERHSPVSSTKTER  1900
1901  HPPVSPSGKTDKRPPVSPSGRTEKHPPVSPGRTEKRLPVSPSGRTDKHQP  1950
1951  VSTAGKTEKHLPVSPSGKTEKQPPVSPTSKTERIEETMSVRELMKAFQSG  2000
2001  QDPSKHKTGLFEHKSAKQKQPQEKGKVRVEKEKGPILTQREAQKTENQTI  2050
2051  KRGQRLPVTGTAESKRGVRVSSIGVKKEDAAGGKEKVLSHKIPEPVQSVP  2100
2101  EEESHRESEVPKEKMADEQGDMDLQISPDRKTSTDFSEVIKQELEDNDKY  2150
2151  QQFRLSEETEKAQLHLDQVLTSPFNTTFPLDYMKDEFLPALSLQSGALDG  2200
2201  SSESLKNEGVAGSPCGSLMEGTPQISSEESYKHEGLAETPETSPESLSFS  2250
2251  PKKSEEQTGETKESTKTETTTEIRSEKEHPTTKDITGGSEERGATVTEDS  2300
2301  ETSTESFQKEATLGSPKDTSPKRQDDCTGSCSVALAKETPTGLTEEAACD  2350
2351  EGQRTFGSSAHKTQTDSEVQESTATSDETKALPLPEASVKTDTGTESKPQ  2400
2401  GVIRSPQGLELALPSRDSEVLSAVADDSLAVSHKDSLEASPVLEDNSSHK  2450
2451  TPDSLEPSPLKESPCRDSLESSPVEPKMKAGIFPSHFPLPAAVAKTELLT  2500
2501  EVASVRSRLLRDPDGSAEDDSLEQTSLMESSGKSPLSPDTPSSEEVSYEV  2550
2551  TPKTTDVSTPKPAVIHECAEEDDSENGEKKRFTPEEEMFKMVTKIKMFDE  2600
2601  LEQEAKQKRDYKKEPKQEESSSSSDPDADCSVDVDEPKHTGSGEDESGVP  2650
2651  VLVTSESRKVSSSSESEPELAQLKKGADSGLLPEPVIRVQPPSPLPSSMD  2700
2701  SNSSPEEVQFQPVVSKQYTFKMNEDTQEEPGKSEEEKDSESHLAEDRHAV  2750
2751  STEAEDRSYDKLNRDTDQPKICDGHGCEAMSPSSSAAPVSSGLQSPTGDD  2800
2801  VDEQPVIYKESLALQGTHEKDTEGEELDVSRAESPQADCPSESFSSSSSL  2850
2851  PHCLVSEGKELDEDISATSSIQKTEVTKTDETFENLPKDCPSQDSSITTQ  2900
2901  TDRFSMDVPVSDLAENDEIYDPQITSPYENVPSQSFFSSEESKTQTDANH  2950
2951  TTSFHSSEVYSVTITSPVEDVVVASSSSGTVLSKESNFEGQDIKMESQQE  3000
3001  STLWEMQSDSVSSSFEPTMSATTTVVGEQISKVIITKTDVDSDSWSEIRE  3050
3051  DDEAFEARVKEEEQKIFGLMVDRQSQGTTPDTTPARTPTEEGTPTSEQNP  3100
3101  FLFQEGKLFEMTRSGAIDMTKRSYADESFHFFQIGQESREETLSEDVKEG  3150
3151  ATGADPLPLETSAESLALSESKETVDDEADLLPDDVSEEVEEIPASDAQL  3200
3201  NSQMGISASTETPTKEAVSVGTKDLPTVQTGDIPPLSGVKQISCPDSSEP  3250
3251  AVQVQLDFSTLTRSVYSDRGDDSPDSSPEEQKSVIEIPTAPMENVPFTES  3300
3301  KSKIPVRTMPTSTPAPPSAEYESSVSEDFLSSVDEENKADEAKPKSKLPV  3350
3351  KVPLQRVEQQLSDLDTSVQKTVAPQGQDMASIAPDNRSKSESDASSLDSK  3400
3401  TKCPVKTRSYTETETESRERAEELELESEEGATRPKILTSRLPVKSRSTT  3450
3451  SSCRGGTSPTKESKEHFFDLYRNSIEFFEEISDEASKLVDRLTQSEREQE  3500
3501  IVSDDESSSALEVSVIENLPPVETEHSVPEDIFDTRPIWDESIETLIERI  3550
3551  PDENGHDHAEDPQDEQERIEERLAYIADHLGFSWTELARELDFTEEQIHQ  3600
3601  IRIENPNSLQDQSHALLKYWLERDGKHATDTNLVECLTKINRMDIVHLME  3650
3651  TNTEPLQERISHSYAEIEQTITLDHSEGFSVLQEELCTAQHKQKEEQAVS  3700
3701  KESETCDHPPIVSEEDISVGYSTFQDGVPKTEGDSSATALFPQTHKEQVQ  3750
3751  QDFSGKMQDLPEESSLEYQQEYFVTTPGTETSETQKAMIVPSSPSKTPEE  3800
3801  VSTPAEEEKLYLQTPTSSERGGSPIIQEPEEPSEHREESSPRKTSLVIVE  3850
3851  SADNQPETCERLDEDAAFEKGDDMPEIPPETVTEEEYIDEHGHTVVKKVT  3900
3901  RKIIRRYVSSEGTEKEEIMVQGMPQEPVNIEEGDGYSKVIKRVVLKSDTE  3950
3951  QSEDNNE                                             3957

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.)

Go back to the NucPred Home Page.