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

NucPred

Fetching Q8BPN8 from www.uniprot.org...

The NucPred score for your sequence is 0.88 (see score help below)

   1  MHLHQVLTGAVNPGDNCYSVGSVGDVPFTAYGSGCDIVILASDFECVQII    50
  51  PGAKHGNIQVSCVECSNQHGRVAASYGNAVCIFEPLGVNSHKRNSQLKCQ   100
 101  WLKTGQFFLSSVTYNLAWDPQDNRLLTATDSIQLWAPPGGDILEEEEDVD   150
 151  NRAPPVLNDWKCIWQCKTSVSVHLMEWSPDGEYFATAGKDDCLLKVWYPM   200
 201  TGWKSSIIPQDPHEVKRRRASTQFSFVYLAHPRAVTGFSWRKTSKYMPRG   250
 251  SVCNVLLTSCHDGVCRLWAETLLPEDCLLGEQICETTTSSVASNLSSAGK   300
 301  HKDRIQHALETIHHLKNLRKGQRRSSVLVTHAELMPDKTATHEVHRHISH   350
 351  HANALCHFHIAASINPTTDIPNVLVGTAFNIDDINGGFVVHWLNNKEFHF   400
 401  TSSTEIFMHQLRKLSEKQLDHESDDADREDEERSQDERERGLRMKLDHEL   450
 451  SLDRESEAGTGSSEHEDGEREGSPRTHPRPSISMPLPTVLLDRKIETLLT   500
 501  EWNKNPDMLFTIHPVDGTFLVWHVKYLDEYNPGIFRQVQVSFSSRIPVAF   550
 551  PSGDANSLSKNIMMYACVNATKDSYNPSQQEMMSVDSPHGSQLHSPSHST   600
 601  DMNILAPTVMMVSKHIDGSLNQWAVTFADKSAFTTVLTVSHKFRYCGHRF   650
 651  HLNDLACHSVLPLLLTSSHHNALLTPESDCQWDSDSKVNRLIDPVKHTKA   700
 701  SSKQPLRNAATRTFHDPNAIYSELILWRVDPIGPLSYTGGVSELARINSL   750
 751  HTSAFSNVAWLPTLIPSYCLGTYCNSASACFVASDGKNLRLYQAVVDARK   800
 801  LLDELSDPEASKLIGEVFNIVSQQSTARPGCIIELDAITDQCGSNTQLLH   850
 851  VFQEDFIIGYKPHKEDMEKKEKESEIFFQPSQGYRPPPFSEKFFLVVIEK   900
 901  DGNNNSILHMWHLHLKSVQACLAKAAEGISSDSLLSVPGQKNLDSSPETS   950
 951  SSMSSVPHSSSIANLQTASKLILSSRLVYSQPLDLPEAVEVIRATPSAGH  1000
1001  LSSSSIYPVCLAPYLVVTTCSDNKVRFWKCCMETNSLGNTSDESETYHWR  1050
1051  RWPLMNDEGEDNSSTVSIVGRPVAVSCSYTGRLAVAYKQPIHHNGFISKE  1100
1101  FSMHVCIFECESTGGSEWVLEQTIHLDDLVKVGSVLDSRVSVDSNLFVYS  1150
1151  KSDAFLSKDRYLIPNIKHLVHLDWVSKEDGSHILTVGVGANIFMYGRLSG  1200
1201  IVSDQTNSKDGVAVITLPLGGSIKQGVKSRWVLLRSIDLVSSVDGTPSLP  1250
1251  VSLSWVRDGILVVGMDCEMHVYAQWKHSVKFGNVDADSPVEETIQDHSAL  1300
1301  KSSMLARKSIVEGAAIPDDVFCSPTVVQDGGLFEAAHALSPTLPQYHPTQ  1350
1351  LLELMDLGKVRRAKAILSHLVKCIAGEVAIVRDPDAGEGTKRHLSRTISV  1400
1401  SGSTAKDTVTIGKDGTRDYTEIDSIPPLPLHALLAADQDTSYRISEDSTK  1450
1451  KPQSYEDHIESQSEDQYSELFQVQEITTDDIDLEPEKRENKSKVINLSQY  1500
1501  GPACFGQEHARVLSSHLMHSSLPGLTRLEQMFLVALADTVATTSTELDEN  1550
1551  RDKNYSGRDTLDECGLRYLLAMRLHTCLLTSLPPLYRVQLLHQGVSTCHF  1600
1601  AWAFHSEAEEELINMIPAIQRGDPQWSELRAMGIGWWVRNVNTLRRCIEK  1650
1651  VAKAAFQRNNEALDAALFYLSMKKKAVVWGLFRSQHDEKMTTFFSHNFNE  1700
1701  DRWRKAALKNAFSLLGKQRFEQSAAFFLLAGSLKDAIEVCLEKMEDIQLA  1750
1751  MVIARLFESEFETSSTYISILNQKILGCQKDGTGFDCKRLHPDPFLRSLA  1800
1801  YWVVKDYTRALDTLLEQTPKEDDEQQVIIKSCNPVVFSFYNYLRTHPLLI  1850
1851  RRNLASPEGTLATLGLKTEKNIADKINLIERKLFFTTANAHFKVGCPVLA  1900
1901  LEVLSKIPKVTKISSLTAKKDQLDSVSGRMENGPSESKPVSRSDGGSGAD  1950
1951  WSAVTSSQFDWSQPMVTVDEEPLRLDWGDDHDGALEEDDGGGLVMKTTDA  2000
2001  KKAGQEQSASDPRALLTPQDEECADGDTEVDVIAEQLKFRACLKILMTEL  2050
2051  RTLATGYEVDGGKLRFQLYNWLEKEIAALHEICNHESVIKEYSSKAHSTV  2100
2101  ETERLDQEEMVDKPDIGSYERHQIERRRLQAKREHAERRKLWLQKNQDLL  2150
2151  RVFLSYCSLHGAQGGGLASVRMELKFLLQESQQETTVKQLQSPLPLPTTL  2200
2201  PLLSASIASTKTVIANPVLYLNNHIHDILYTIVQMKTPPHPSVEDVKVHT  2250
2251  LHSLAASLSASIYQALCDSHSYSQSEGNQFTGMAYQGLLLSDRRRLRTES  2300
2301  IEEHATPNSAPAQWPGVSSLINLLSSAQDEDQPKLNVLLCEAVVAVYLSL  2350
2351  LIHALATNSSNELFRLAAHPLNNRMWAAVFGGGVKLVVKPRRQSESIAAP  2400
2401  PVASEDMDKHRRRFNMRMLVPGRPVKDATPPPVPAERPSYKEKFIPPELS  2450
2451  MWDYFVAKPFLPLSDSGVIYDSDESVHSDDEEDDAFFSDTQIQEHQDPNS  2500
2501  YSWALLHLTMVKLALHNIKNFFPIAGLEFSELPVTSPLGIAVIKNLENWE  2550
2551  QILQEKMDHFEGPPPNYVNTYPTDLSVGAGPAILRNKAMLEPENTPFKSR  2600
2601  DSSALPVKRLWHFLVKQEVLQETFIRYIFTKKRKQSEVEADLGYPGGKAK  2650
2651  VIHKESDMIMAFSINKANCNEIVLASTHDVQELDVTSLLACQSYIWIGEE  2700
2701  YDRESKSSDDIDYRGSTTTLYQPGAASHSSSQPHPPPSLPWLGSGQTSTG  2750
2751  ATVLMKRNLHNVKRMTSHPVHQYYLTGAQDGSVRMFEWTRPQQLVCFRQA  2800
2801  GNARVTRLYFNSQGNKCGVADGEGFLSIWQVNQTASNPKPYMSWQCHSKA  2850
2851  TSDFAFITSSSLVATSGHSNDNRNVCLWDTLISPGNSLIHGFTCHDHGAT  2900
2901  VLQYAPKQQLLISGGRKGYICIFDIRQRQLIHTFQAHDSAIKALALDSCE  2950
2951  EYFTTGSAEGNIKVWRLTGHGLIHSFKSEHAKQSIFRNIGAGVMQIAISQ  3000
3001  DNRLFSCGADGTLKTRVLPSAFNIPNRILDIL                    3032

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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