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

NucPred

Fetching Q8WUY3 from www.uniprot.org...

The NucPred score for your sequence is 0.82 (see score help below)

   1  MEEFLQRAKSKLNRSKRLEKVHVVIGPKSCDLDSLISTFTYAYFLDKVSP    50
  51  PGVLCLPVLNIPRTEFNYFTETRFILEELNISESFHIFRDEINLHQLNDE   100
 101  GKLSITLVGSSVLASEDKTLESAVVKVINPVEQSDANVEFRESSSSLVLK   150
 151  EILQEAPELITEQLAHRLRGSILFKWMTMESEKISEKQEEILSILEEKFP   200
 201  NLPPREDIINVLQETQFSAQGLSIEQTMLKDLKELSDGEIKVAISTVSMN   250
 251  LENCLFHSNITSDLKAFTDKFGFDVLILFSSYLSEEQQPRRQIAVYSENM   300
 301  ELCSQICCELEECQNPCLELEPFDCGCDEILVYQQEDPSVTCDQVVLVVK   350
 351  EVINRRCPEMVSNSRTSSTEAVAGSAPLSQGSSGIMELYGSDIEPQPSSV   400
 401  NFIENPPDLNDSNQAQVDANVDLVSPDSGLATIRSSRSSKESSVFLSDDS   450
 451  PVGEGAGPHHTLLPGLDSYSPIPEGAVAEEHAWSGEHGEHFDLFNFDPAP   500
 501  MASGQSQQSSHSADYSPADDFFPNSDLSEGQLPAGPEGLDGMGTNMSNYS   550
 551  SSSLLSGAGKDSLVEHDEEFVQRQDSPRDNSERNLSLTDFVGDESPSPER   600
 601  LKNTGKRIPPTPMNSLVESSPSTEEPASLYTEDMTQKATDTGHMGPPQTH   650
 651  ARCSSWWGGLEIDSKNIADAWSSSEQESVFQSPESWKEHKPSSIDRRASD   700
 701  SVFQPKSLEFTKSGPWESEFGQPELGSNDIQDKNEESLPFQNLPMEKSPL   750
 751  PNTSPQGTNHLIEDFASLWHSGRSPTAMPEPWGNPTDDGEPAAVAPFPAW   800
 801  SAFGKEDHDEALKNTWNLHPTSSKTPSVRDPNEWAMAKSGFAFSSSELLD   850
 851  NSPSEINNEAAPEIWGKKNNDSRDHIFAPGNPSSDLDHTWTNSKPPKEDQ   900
 901  NGLVDPKTRGKVYEKVDSWNLFEENMKKGGSDVLVPWEDSFLSYKCSDYS   950
 951  ASNLGEDSVPSPLDTNYSTSDSYTSPTFAGDEKETEHKPFAKEEGFESKD  1000
1001  GNSTAEETDIPPQSLQQSSRNRISSGPGNLDMWASPHTDNSSEINTTHNL  1050
1051  DENELKTEHTDGKNISMEDDVGESSQSSYDDPSMMQLYNETNRQLTLLHS  1100
1101  STNSRQTAPDSLDLWNRVILEDTQSTATISDMDNDLDWDDCSGGAAIPSD  1150
1151  GQTEGYMAEGSEPETRFTVRQLEPWGLEYQEANQVDWELPASDEHTKDSA  1200
1201  PSEHHTLNEKSGQLIANSIWDSVMRDKDMSSFMLPGSSHITDSEQRELPP  1250
1251  EIPSHSANVKDTHSPDAPAASGTSESEALISHLDKQDTERETLQSDAASL  1300
1301  ATRLENPGYFPHPDPWKGHGDGQSESEKEAQGATDRGHLDEEEVIASGVE  1350
1351  NASGISEKGQSDQELSSLVASEHQEICIKSGKISSLAVTFSPQTEEPEEV  1400
1401  LEYEEGSYNLDSRDVQTGMSADNLQPKDTHEKHLMSQRNSGETTETSDGM  1450
1451  NFTKYVSVPEKDLEKTEECNFLEPENVGGGPPHRVPRSLDFGDVPIDSDV  1500
1501  HVSSTCSEITKNLDVKGSENSLPGAGSSGNFDRDTISSEYTHSSASSPEL  1550
1551  NDSSVALSSWGQQPSSGYQEENQGNWSEQNHQESELITTDGQVEIVTKVK  1600
1601  DLEKNRINEFEKSFDRKTPTFLEIWNDSVDGDSFSSLSSPETGKYSEHSG  1650
1651  THQESNLIASYQEKNEHDISATVQPEDARVISTSSGSDDDSVGGEESIEE  1700
1701  EIQVANCHVAEDESRAWDSLNESNKFLVTADPKSENIYDYLDSSEPAENE  1750
1751  NKSNPFCDNQQSSPDPWTFSPLTETEMQITAVEKEKRSSPETGTTGDVAW  1800
1801  QISPKASFPKNEDNSQLEMLGFSADSTEWWKASPQEGRLIESPFERELSD  1850
1851  SSGVLEINSSVHQNASPWGVPVQGDIEPVETHYTNPFSDNHQSPFLEGNG  1900
1901  KNSHEQLWNIQPRQPDPDADKFSQLVKLDQIKEKDSREQTFVSAAGDELT  1950
1951  PETPTQEQCQDTMLPVCDHPDTAFTHAEENSCVTSNVSTNEGQETNQWEQ  2000
2001  EKSYLGEMTNSSIATENFPAVSSPTQLIMKPGSEWDGSTPSEDSRGTFVP  2050
2051  DILHGNFQEGGQLASAAPDLWIDAKKPFSLKADGENPDILTHCEHDSNSQ  2100
2101  ASDSPDICHDSEAKQETEKHLSACMGPEVESSELCLTEPEIDEEPIYEPG  2150
2151  REFVPSNAELDSENATVLPPIGYQADIKGSSQPASHKGSPEPSEINGDNS  2200
2201  TGLQVSEKGASPDMAPILEPVDRRIPRIENVATSIFVTHQEPTPEGDGSW  2250
2251  ISDSFSPESQPGARALFDGDPHLSTENPALVPDALLASDTCLDISEAAFD  2300
2301  HSFSDASGLNTSTGTIDDMSKLTLSEGHPETPVDGDLGKQDICSSEASWG  2350
2351  DFEYDVMGQNIDEDLLREPEHFLYGGDPPLEEDSLKQSLAPYTPPFDLSY  2400
2401  LTEPAQSAETIEEAGSPEDESLGCRAAEIVLSALPDRRSEGNQAETKNRL  2450
2451  PGSQLAVLHIREDPESVYLPVGAGSNILSPSNVDWEVETDNSDLPAGGDI  2500
2501  GPPNGASKEISELEEEKTIPTKEPEQIKSEYKEERCTEKNEDRHALHMDY  2550
2551  ILVNREENSHSKPETCEERESIAELELYVGSKETGLQGTQLASFPDTCQP  2600
2601  ASLNERKGLSAEKMSSKSDTRSSFESPAQDQSWMFLGHSEVGDPSLDARD  2650
2651  SGPGWSGKTVEPFSELGLGEGPQLQILEEMKPLESLALEEASGPVSQSQK  2700
2701  SKSRGRAGPDAVTLQAVTHDNEWEMLSPQPVQKNMIPDTEMEEETEFLEL  2750
2751  GTRISRPNGLLSEDVGMDIPFEEGVLSPSAADMRPEPPNSLDLNDTHPRR  2800
2801  IKLTAPNINLSLDQSEGSILSDDNLDSPDEIDINVDELDTPDEADSFEYT  2850
2851  GHDPTANKDSGQESESIPEYTAEEEREDNRLWRTVVIGEQEQRIDMKVIE  2900
2901  PYRRVISHGGYYGDGLNAIIVFAACFLPDSSRADYHYVMENLFLYVISTL  2950
2951  ELMVAEDYMIVYLNGATPRRRMPGLGWMKKCYQMIDRRLRKNLKSFIIVH  3000
3001  PSWFIRTILAVTRPFISSKFSSKIKYVNSLSELSGLIPMDCIHIPESIIK  3050
3051  LDEELREASEAAKTSCLYNDPEMSSMEKDIDLKLKEKP              3088

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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