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

NucPred

Fetching Q8TDJ6 from www.uniprot.org...

The NucPred score for your sequence is 0.87 (see score help below)

   1  MHLHQVLTGAVNPGDNCYSVGSVGDVPFTAYGSGCDIVILANDFECVQII    50
  51  PGAKHGNIQVSCVECSNQQGRIAASYGNAVCIFEPLGINSHKRNCQLKCQ   100
 101  WLKTGQFFLSSVTYNLAWDPQDNRLLTATDSIQLWAPPGDDILEEEEEID   150
 151  NTVPPVLNDWKCVWQCKTSVSVHLMEWSPDGEYFATAGKDDCLLKVWYPM   200
 201  TGWKSSIIPQDHHEVKRRQSSTQFSFVYLAHPRAVTGFSWRKTSKYMPRG   250
 251  SVCNVLLTSCHDGVCRLWAETLLPEDCLLGEQICETTTSSIASSLSHAGR   300
 301  HKDRIQHALETIHHLKNLRKGQRRSSVLVTHAELMPDQTAMHEVQRHISH   350
 351  HANALCHFHIAASINPATDIPNVLVGTAFNVDDGNGGFVVHWLNNKEFHF   400
 401  TSSTEVFMHQLRKLSDKQVDHENDDADREDEEHSQEDRERGLHMKLDHDL   450
 451  SLDRESEAGTGSSEHEDGEREGSPRTYSRLSVPMPLPTVLLDRKIETLLT   500
 501  EWNKNPDMLFTIHPVDGTFLVWHVKYLDEYNPGIFRQVQVSFSSRIPVAF   550
 551  PSGDASSLSKNIMMYACINATKDSHHTLLHQEGMSVGSPHGSQPHSRSHS   600
 601  THMNILAPTVMMISKHIDGSLNQWAVTFADKSAFTTVLTVSHKFRYCGHR   650
 651  FHLNDLACHSVLPLLLTSSHHNALLTPELDCQWDSDNKLSRLMDPVKHIK   700
 701  GSSKQPLRNAATRTFHDPNAIYSELILWRVDPIGPLSYTGGVSELARINS   750
 751  LHTSAFSNVAWLPTLIPSYCLGTYCNSASACFVASDGKNLRLYQAVVDAR   800
 801  KLLDELSDPESSKLIGEVFNIVSQQSTARPGCIIELDAITNQCGSNTQLL   850
 851  HVFQEDFIIGYKPHKEDMEKKETEIFFQPSQGYRPPPFSEKFFLVVIEKD   900
 901  SNNNSILHMWHLHLKSVQACLAKASEGASSESLLSVPGQKNVDSSPETSP   950
 951  SVSPMPHSSSIANLQTASKLILSSRLVYSQPLDLPESVEVIRATPSAGHL  1000
1001  SSSSIYPVCLAPYLVVTTCSDNKVRFWKCCMEANPECNKSDEKEIYHWKR  1050
1051  WPLMNDEGEDNSSTVSIVGRPVAVSCSYTGRLAVAYKQPIHHNGFVSKEF  1100
1101  SMHVCIFECESTGGSEWVLEQTIHLDDLVKVGSVLDSRVSVDSNLFVYSK  1150
1151  SDALLSKDRYLIPNIKHLVHLDWVSKEDGSHILTVGVGANIFMYGRLSGI  1200
1201  VTEQTNSKDGVAVITLPLGGSIKQGVKSRWVLLRSIDLVSSVDGTPSLPV  1250
1251  SLSWVRDGILVVGMDCEMHVYAQWKHAVKFGDTEADSSNAEEAAMQDHST  1300
1301  FKSNMLARKSVVEGTAISDDVFCSPTVIQDGGLFEAAHVLSPTLPQYHPT  1350
1351  QLLELMDLGKVRRAKAILSHLVKCIAGEVAIVRDPDAGEGTKRHLSRTIS  1400
1401  VSGSTAKETVTVGKDGTRDYTEIDSIPPLPLYALLAADQDTSYRISEEST  1450
1451  KIPQSYEDQTVSQPEDQYSELFQIQDIPTDDIDLEPEKRENKSKVINLSQ  1500
1501  YGPAYFGQEHARVLSSHLMHSSLPGLTRLEQMFLVALADTVATTSTELDE  1550
1551  SRDKSCSGRDTLDECGLRYLLAMRLHTCLLTSLPPLYRVQLLHQGVSTCH  1600
1601  FAWAFHSEAEEELINMIPAIQRGDPQWSELRAMGIGWWVRNINTLRRCIE  1650
1651  KVAKASFQRNNDALDAALFYLSMKKKAVVWGLFRSQHDEKMTTFFSHNFN  1700
1701  EDRWRKAALKNAFSLLGKQRFEQSAAFFLLAGSLKDAIEVCLEKMEDIQL  1750
1751  AMVIARLYESEFETSSTYISILNQKILGCQKDGSGFSCKRLHPDPFLRSL  1800
1801  AYWVMKDYTRALDTLLEQTPKEDDEHQVIIKSCNPVAFSFYNYLRTHPLL  1850
1851  IRRNLASPEGTLATLGLKTEKNFVDKINLIERKLFFTTANAHFKVGCPVL  1900
1901  ALEVLSKIPKVTKTSALSAKKDQPDFISHRMDDVPSHSKALSDGNGSSGI  1950
1951  EWSNVTSSQYDWSQPIVKVDEEPLNLDWGEDHDSALDEEEDDAVGLVMKS  2000
2001  TDAREKDKQSDQKASDPNMLLTPQEEDDPEGDTEVDVIAEQLKFRACLKI  2050
2051  LMTELRTLATGYEVDGGKLRFQLYNWLEKEIAALHEICNHESVIKEYSSK  2100
2101  TYSKVESDLLDQEEMVDKPDIGSYERHQIERRRLQAKREHAERRKSWLQK  2150
2151  NQDLLRVFLSYCSLHGAQGGGLASVRMELKFLLQESQQETTVKQLQSPLP  2200
2201  LPTTLPLLSASIASTKTVIANPVLYLNNHIHDILYTIVQMKTPPHPSIED  2250
2251  VKVHTLHSLAASLSASIYQALCDSHSYSQTEGNQFTGMAYQGLLLSDRRR  2300
2301  LRTESIEEHATPNSSPAQWPGVSSLINLLSSAQDEDQPKLNILLCEAVVA  2350
2351  VYLSLLIHALATNSSSELFRLAAHPLNNRMWAAVFGGGVKLVVKPRRQSE  2400
2401  NISAPPVLSEDIDKHRRRFNMRMLVPGRPVKDATPPPVPAERPSYKEKFI  2450
2451  PPELSMWDYFVAKPFLPLSDSGVIYDSDESIHSDEEDDAFFSDTQIQEHQ  2500
2501  DPNSYSWALLHLTMVKLALHNVKNFFPIAGLEFSELPVTSPLGIAVIKNL  2550
2551  ENWEQILQEKMDQFEGPPPNYINTYPTDLSVGAGPAILRNKAMLEPENTP  2600
2601  FKSRDSSAFPVKRLWHFLVKQEVLQETFIRYIFTKKRKQSEVEADLGYPG  2650
2651  GKAKVIHKESDMIMAFSVNKANCNEIVLASTHDVQELDVTSLLACQSYIW  2700
2701  IGEEYDRESKSSDDVDYRGSTTTLYQPSATSYSASQVHPPSSLPWLGTGQ  2750
2751  TSTGASVLMKRNLHNVKRMTSHPVHQYYLTGAQDGSVRMFEWTRPQQLVC  2800
2801  FRQAGNARVTRLYFNSQGNKCGVADGEGFLSIWQVNQTASNPKPYMSWQC  2850
2851  HSKATSDFAFITSSSLVATSGHSNDNRNVCLWDTLISPGNSLIHGFTCHD  2900
2901  HGATVLQYAPKQQLLISGGRKGHVCIFDIRQRQLIHTFQAHDSAIKALAL  2950
2951  DPYEEYFTTGSAEGNIKVWRLTGHGLIHSFKSEHAKQSIFRNIGAGVMQI  3000
3001  DIIQGNRLFSCGADGTLKTRVLPNAFNIPNRILDIL                3036

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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