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

NucPred

Fetching Q62388 from www.uniprot.org...

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

   1  MSLALNDLLICCRQLEHDRATERRKEVDKFKRLIQDPETVQHLDRHSDSK    50
  51  QGKYLNWDAVFRFLQKYIQKEMESLRTAKSNVSATTQSSRQKKMQEISSL   100
 101  VRYFIKCANKRAPRLKCQDLLNYVMDTVKDSSNGLTYGADCSNILLKDIL   150
 151  SVRKYWCEVSQQQWLELFSLYFRLYLKPSQDINRVLVARIIHAVTRGCCS   200
 201  QTDGLPSKFLDLFSKAIQYARQEKSSPGLSHILAALNIFLKSLAVNFRKR   250
 251  VCEAGDEILPTLLYIWTQHRLNDSLKEVIIELIQLQIYIHHPQGARAPEE   300
 301  GAYESMKWKSILYNLYDLLVNEISHIGSRGKYSSGSRNIAVKENLIDLMA   350
 351  DICYQLFDADTRSVEISQSYVTQRESTDYSVPCKRRKIDVGWEVIKDYLQ   400
 401  KSQSDFDLVPWLQITTRLISKYPSSLPNCELSPLILILYQLLPQQRRGER   450
 451  IPYVLRCLKEVALCQGKKSNLESSQKSDLLKLWIKIWSITFRGISSGQTQ   500
 501  TENFGLLEAIIQGSLVELDREFWKLFTGSACKPSSPSVCCLTLALSICVV   550
 551  PDAIKMGTEQSVCEANRSFSVKESIMRWLLFYQLEDDLEDSTELPPILQS   600
 601  NFPHLVVEKILVSLTMKNSKAAMKFFQSVPECEQHCEDKEEPSFSEVEEL   650
 651  FLQTTFDKMDFLTTVKEYAVEKFQSSVGFSVQQNLKESLDHYLLGLSEQL   700
 701  LSNYSSEITSSETLVRCSSLLVGVLGCYCYMGIITEDEAHKSELFQKAKS   750
 751  LMQCAGESISLFKNKTNEESRIGSLRNVMHLCTSCLCIHTKHTPNKIASG   800
 801  FFLRLLTSKLMNDIADICKSLASCTKKPLDHGVHPGEDDEDGGGCDSLME   850
 851  AEGPSSTGLSTAYPASSVSDANDYGENQNAVGAMSPLAADYLSKQDHLLL   900
 901  DMLRFLGRSVTASQSHTVSFRGADIRRKLLLLLDSSILDLMKPLHLHMYL   950
 951  VLLKDLPGNEHSLPMEDVVELLQPLSLVCSLHRRDQDVCKTILSNVLHIV  1000
1001  TNLGQGSVDMESTRIAQGHFLTVMGAFWHLTKEKKCVFSVRMALVKCLQT  1050
1051  LLEADPYSEWAILNVKGQDFPVNEAFSQFLADDHHQVRMLAAGSVNRLFQ  1100
1101  DMRQGDFSRSLKALPLKFQQTSFNNAYTTAEAGIRGLLCDSQNPDLLDEI  1150
1151  YNRKSVLLMMIAVVLHCSPVCEKQALFALCKSVKENRLEPHLVKKVLEKV  1200
1201  SESFGCRSLEDFMISHLDYLVLEWLNLQDTEYSLSSFPFMLLNYTSIEDF  1250
1251  YRSCYKILIPHLVIRSHFDEVKSIANQIQKCWKSLLVDCFPKILVHILPY  1300
1301  FAYEGTRDSYVSQKRETATKVYDTLKGEDFLGKQIDQVFISNLPEIVVEL  1350
1351  LMTLHETADSADSDASQSATALCDFSGDLDPAPNPPYFPSHVIQATFAYI  1400
1401  SNCHKTKFKSILEILSKIPDSYQKILLAICEQAAETNNVFKKHRILKIYH  1450
1451  LFVSLLLKDIQSGLGGAWAFVLRDVIYTLIHYINKRSSHFTDVSLRSFSL  1500
1501  CCDLLSRVCHTAVTQCKDALESHLHVIVGTLIPLVDYQEVQEQVLDLLKY  1550
1551  LVIDNKDNKNLSVTIKLLDPFPDHVIFKDLRLTQQKIKYSGGPFSLLEEI  1600
1601  NHFLSVSAYNPLPLTRLEGLKDLRRQLEQHKDQMLDLLRASQDNPQDGIV  1650
1651  VKLVVSLLQLSKMAVNQTGEREVLEAVGRCLGEIGPLDFSTIAVQHNKDV  1700
1701  SYTKAYGLPEDRELQWTLIMLTALNNTLVEDSVKIRSAAATCLKNILATK  1750
1751  IGHIFWENYKTSADPMLTYLQPFRTSRKKFLEVPRSVKEDVLEGLDAVNL  1800
1801  WVPQSESHDIWIKTLTCAFLDSGGINSEILQLLKPMCEVKTDFCQMLLPY  1850
1851  LIHDVLLQDTHESWRTLLSAHVRGFFTSCFKHSSQASRSATPANSDSESE  1900
1901  NFLRCCLDKKSQRTMLAVVDYLRRQKRPSSGTAFDDAFWLDLNYLEVAKV  1950
1951  AQSCSAHFTALLYAEIYSDKKSTDEQEKRSPTFEEGSQGTTISSLSEKSK  2000
2001  EETGISLQDLLLEIYRSIGEPDSLYGCGGGKMLQPLTRIRTYEHEATWEK  2050
2051  ALVTYDLETSISSSTRQSGIIQALQNLGLSHILSVYLKGLDYERREWCAE  2100
2101  LQELRYQAAWRNMQWGLCASAGQEVEGTSYHESLYNALQCLRNREFSTFY  2150
2151  ESLRYASLFRVKEVEELSKGSLESVYSLYPTLSRLQAIGELENSGELFSR  2200
2201  SVTDRERSEAYWKWQKHSQLLKDSDFSFQEPLMALRTVILETLVQKEMER  2250
2251  SQGACSKDILTKHLVEFSVLARTFKNTQLPERAIFKIKQYNSAICGISEW  2300
2301  HLEEAQVFWAKKEQSLALSILKQMIKKLDSSFKDKENDAGLKVIYAECLR  2350
2351  VCGSWLAETCLENPAVIMQTYLEKAVKVAGSYDGNSRELRNGQMKAFLSL  2400
2401  ARFSDTQYQRIENYMKSSEFENKQTLLKRAKEEVGLLREHKIQTNRYTVK  2450
2451  VQRELELDECALRALREDRKRFLCKAVENYINCLLSGEEHDLWVFRLCSL  2500
2501  WLENSGVSEVNGMMKKDGMKISSYKFLPLMYQLAARMGTKMTGGLGFHEV  2550
2551  LNNLISRISLDHPHHTLFIILALANANKDEFLSKPETTRRSRITKSTSKE  2600
2601  NSHLDEDRTEAATRIIHSIRSKRCKMVKDMEALCDAYIILANMDASQWRA  2650
2651  QRKGINIPANQPITKLKNLEDVVVPTMEIKVDPTGEYENLVTIKSFKTEF  2700
2701  RLAGGLNLPKIIDCVGSDGKERRQLVKGRDDLRQDAVMQQVFQMCNTLLQ  2750
2751  RNTETRKRKLTICTYKVVPLSQRSGVLEWCTGTVPIGEYLVNSEDGAHRR  2800
2801  YRPNDFSANQCQKKMMEVQKKSFEEKYDTFMTICQNFEPVFRYFCMEKFL  2850
2851  DPAVWFEKRLAYTRSVATSSIVGYILGLGDRHVQNILINEQSAELVHIDL  2900
2901  GVAFEQGKILPTPETVPFRLSRDIVDGMGITGVEGVFRRCCEKTMEVMRS  2950
2951  SQETLLTIVEVLLYDPLFDWTMNPLKALYLQQRPEDESDLHSTPNADDQE  3000
3001  CKQSLSDTDQSFNKVAERVLMRLQEKLKGVEEGTVLSVGGQVNLLIQQAM  3050
3051  DPKNLSRLFPGWKAWV                                    3066

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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