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

NucPred

Fetching Q9P2D1 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MADPGMMSLFGEDGNIFSEGLEGLGECGYPENPVNPMGQQMPIDQGFASL    50
  51  QPSLHHPSTNQNQTKLTHFDHYNQYEQQKMHLMDQPNRMMSNTPGNGLAS   100
 101  PHSQYHTPPVPQVPHGGSGGGQMGVYPGMQNERHGQSFVDSSSMWGPRAV   150
 151  QVPDQIRAPYQQQQPQPQPPQPAPSGPPAQGHPQHMQQMGSYMARGDFSM   200
 201  QQHGQPQQRMSQFSQGQEGLNQGNPFIATSGPGHLSHVPQQSPSMAPSLR   250
 251  HSVQQFHHHPSTALHGESVAHSPRFSPNPPQQGAVRPQTLNFSSRSQTVP   300
 301  SPTINNSGQYSRYPYSNLNQGLVNNTGMNQNLGLTNNTPMNQSVPRYPNA   350
 351  VGFPSNSGQGLMHQQPIHPSGSLNQMNTQTMHPSQPQGTYASPPPMSPMK   400
 401  AMSNPAGTPPPQVRPGSAGIPMEVGSYPNMPHPQPSHQPPGAMGIGQRNM   450
 451  GPRNMQQSRPFIGMSSAPRELTGHMRPNGCPGVGLGDPQAIQERLIPGQQ   500
 501  HPGQQPSFQQLPTCPPLQPHPGLHHQSSPPHPHHQPWAQLHPSPQNTPQK   550
 551  VPVHQHSPSEPFLEKPVPDMTQVSGPNAQLVKSDDYLPSIEQQPQQKKKK   600
 601  KKNNHIVAEDPSKGFGKDDFPGGVDNQELNRNSLDGSQEEKKKKKRSKAK   650
 651  KDPKEPKEPKEKKEPKEPKTPKAPKIPKEPKEKKAKTATPKPKSSKKSSN   700
 701  KKPDSEASALKKKVNKGKTEGSENSDLDKTPPPSPPPEEDEDPGVQKRRS   750
 751  SRQVKRKRYTEDLEFKISDEEADDADAAGRDSPSNTSQSEQQESVDAEGP   800
 801  VVEKIMSSRSVKKQKESGEEVEIEEFYVKYKNFSYLHCQWASIEDLEKDK   850
 851  RIQQKIKRFKAKQGQNKFLSEIEDELFNPDYVEVDRIMDFARSTDDRGEP   900
 901  VTHYLVKWCSLPYEDSTWERRQDIDQAKIEEFEKLMSREPETERVERPPA   950
 951  DDWKKSESSREYKNNNKLREYQLEGVNWLLFNWYNMRNCILADEMGLGKT  1000
1001  IQSITFLYEIYLKGIHGPFLVIAPLSTIPNWEREFRTWTELNVVVYHGSQ  1050
1051  ASRRTIQLYEMYFKDPQGRVIKGSYKFHAIITTFEMILTDCPELRNIPWR  1100
1101  CVVIDEAHRLKNRNCKLLEGLKMMDLEHKVLLTGTPLQNTVEELFSLLHF  1150
1151  LEPSRFPSETTFMQEFGDLKTEEQVQKLQAILKPMMLRRLKEDVEKNLAP  1200
1201  KEETIIEVELTNIQKKYYRAILEKNFTFLSKGGGQANVPNLLNTMMELRK  1250
1251  CCNHPYLINGAEEKILEEFKETHNAESPDFQLQAMIQAAGKLVLIDKLLP  1300
1301  KLKAGGHRVLIFSQMVRCLDILEDYLIQRRYPYERIDGRVRGNLRQAAID  1350
1351  RFSKPDSDRFVFLLCTRAGGLGINLTAADTCIIFDSDWNPQNDLQAQARC  1400
1401  HRIGQSKSVKIYRLITRNSYEREMFDKASLKLGLDKAVLQSMSGRENATN  1450
1451  GVQQLSKKEIEDLLRKGAYGALMDEEDEGSKFCEEDIDQILLRRTHTITI  1500
1501  ESEGKGSTFAKASFVASGNRTDISLDDPNFWQKWAKKAELDIDALNGRNN  1550
1551  LVIDTPRVRKQTRLYSAVKEDELMEFSDLESDSEEKPCAKPRRPQDKSQG  1600
1601  YARSECFRVEKNLLVYGWGRWTDILSHGRYKRQLTEQDVETICRTILVYC  1650
1651  LNHYKGDENIKSFIWDLITPTADGQTRALVNHSGLSAPVPRGRKGKKVKA  1700
1701  QSTQPVVQDADWLASCNPDALFQEDSYKKHLKHHCNKVLLRVRMLYYLRQ  1750
1751  EVIGDQADKILEGADSSEADVWIPEPFHAEVPADWWDKEADKSLLIGVFK  1800
1801  HGYEKYNSMRADPALCFLERVGMPDAKAIAAEQRGTDMLADGGDGGEFDR  1850
1851  EDEDPEYKPTRTPFKDEIDEFANSPSEDKEESMEIHATGKHSESNAELGQ  1900
1901  LYWPNTSTLTTRLRRLITAYQRSYKRQQMRQEALMKTDRRRRRPREEVRA  1950
1951  LEAEREAIISEKRQKWTRREEADFYRVVSTFGVIFDPVKQQFDWNQFRAF  2000
2001  ARLDKKSDESLEKYFSCFVAMCRRVCRMPVKPDDEPPDLSSIIEPITEER  2050
2051  ASRTLYRIELLRKIREQVLHHPQLGERLKLCQPSLDLPEWWECGRHDRDL  2100
2101  LVGAAKHGVSRTDYHILNDPELSFLDAHKNFAQNRGAGNTSSLNPLAVGF  2150
2151  VQTPPVISSAHIQDERVLEQAEGKVEEPENPAAKEKCEGKEEEEETDGSG  2200
2201  KESKQECEAEASSVKNELKGVEVGADTGSKSISEKGSEEDEEEKLEDDDK  2250
2251  SEESSQPEAGAVSRGKNFDEESNASMSTARDETRDGFYMEDGDPSVAQLL  2300
2301  HERTFAFSFWPKDRVMINRLDNICEAVLKGKWPVNRRQMFDFQGLIPGYT  2350
2351  PTTVDSPLQKRSFAELSMVGQASISGSEDITTSPQLSKEDALNLSVPRQR  2400
2401  RRRRRKIEIEAERAAKRRNLMEMVAQLRESQVVSENGQEKVVDLSKASRE  2450
2451  ATSSTSNFSSLSSKFILPNVSTPVSDAFKTQMELLQAGLSRTPTRHLLNG  2500
2501  SLVDGEPPMKRRRGRRKNVEGLDLLFMSHKRTSLSAEDAEVTKAFEEDIE  2550
2551  TPPTRNIPSPGQLDPDTRIPVINLEDGTRLVGEDAPKNKDLVEWLKLHPT  2600
2601  YTVDMPSYVPKNADVLFSSFQKPKQKRHRCRNPNKLDINTLTGEERVPVV  2650
2651  NKRNGKKMGGAMAPPMKDLPRWLEENPEFAVAPDWTDIVKQSGFVPESMF  2700
2701  DRLLTGPVVRGEGASRRGRRPKSEIARAAAAAAAVASTSGINPLLVNSLF  2750
2751  AGMDLTSLQNLQNLQSLQLAGLMGFPPGLATAATAGGDAKNPAAVLPLML  2800
2801  PGMAGLPNVFGLGGLLNNPLSAATGNTTTASSQGEPEDSTSKGEEKGNEN  2850
2851  EDENKDSEKSTDAVSAADSANGSVGAATAPAGLPSNPLAFNPFLLSTMAP  2900
2901  GLFYPSMFLPPGLGGLTLPGFPALAGLQNAVGSSEEKAADKAEGGPFKDG  2950
2951  ETLEGSDAEESLDKTAESSLLEDEIAQGEELDSLDGGDEIENNENDE     2997

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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