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

NucPred

Fetching Q13315 from www.uniprot.org...

The NucPred score for your sequence is 0.98 (see score help below)

   1  MSLVLNDLLICCRQLEHDRATERKKEVEKFKRLIRDPETIKHLDRHSDSK    50
  51  QGKYLNWDAVFRFLQKYIQKETECLRIAKPNVSASTQASRQKKMQEISSL   100
 101  VKYFIKCANRRAPRLKCQELLNYIMDTVKDSSNGAIYGADCSNILLKDIL   150
 151  SVRKYWCEISQQQWLELFSVYFRLYLKPSQDVHRVLVARIIHAVTKGCCS   200
 201  QTDGLNSKFLDFFSKAIQCARQEKSSSGLNHILAALTIFLKTLAVNFRIR   250
 251  VCELGDEILPTLLYIWTQHRLNDSLKEVIIELFQLQIYIHHPKGAKTQEK   300
 301  GAYESTKWRSILYNLYDLLVNEISHIGSRGKYSSGFRNIAVKENLIELMA   350
 351  DICHQVFNEDTRSLEISQSYTTTQRESSDYSVPCKRKKIELGWEVIKDHL   400
 401  QKSQNDFDLVPWLQIATQLISKYPASLPNCELSPLLMILSQLLPQQRHGE   450
 451  RTPYVLRCLTEVALCQDKRSNLESSQKSDLLKLWNKIWCITFRGISSEQI   500
 501  QAENFGLLGAIIQGSLVEVDREFWKLFTGSACRPSCPAVCCLTLALTTSI   550
 551  VPGTVKMGIEQNMCEVNRSFSLKESIMKWLLFYQLEGDLENSTEVPPILH   600
 601  SNFPHLVLEKILVSLTMKNCKAAMNFFQSVPECEHHQKDKEELSFSEVEE   650
 651  LFLQTTFDKMDFLTIVRECGIEKHQSSIGFSVHQNLKESLDRCLLGLSEQ   700
 701  LLNNYSSEITNSETLVRCSRLLVGVLGCYCYMGVIAEEEAYKSELFQKAK   750
 751  SLMQCAGESITLFKNKTNEEFRIGSLRNMMQLCTRCLSNCTKKSPNKIAS   800
 801  GFFLRLLTSKLMNDIADICKSLASFIKKPFDRGEVESMEDDTNGNLMEVE   850
 851  DQSSMNLFNDYPDSSVSDANEPGESQSTIGAINPLAEEYLSKQDLLFLDM   900
 901  LKFLCLCVTTAQTNTVSFRAADIRRKLLMLIDSSTLEPTKSLHLHMYLML   950
 951  LKELPGEEYPLPMEDVLELLKPLSNVCSLYRRDQDVCKTILNHVLHVVKN  1000
1001  LGQSNMDSENTRDAQGQFLTVIGAFWHLTKERKYIFSVRMALVNCLKTLL  1050
1051  EADPYSKWAILNVMGKDFPVNEVFTQFLADNHHQVRMLAAESINRLFQDT  1100
1101  KGDSSRLLKALPLKLQQTAFENAYLKAQEGMREMSHSAENPETLDEIYNR  1150
1151  KSVLLTLIAVVLSCSPICEKQALFALCKSVKENGLEPHLVKKVLEKVSET  1200
1201  FGYRRLEDFMASHLDYLVLEWLNLQDTEYNLSSFPFILLNYTNIEDFYRS  1250
1251  CYKVLIPHLVIRSHFDEVKSIANQIQEDWKSLLTDCFPKILVNILPYFAY  1300
1301  EGTRDSGMAQQRETATKVYDMLKSENLLGKQIDHLFISNLPEIVVELLMT  1350
1351  LHEPANSSASQSTDLCDFSGDLDPAPNPPHFPSHVIKATFAYISNCHKTK  1400
1401  LKSILEILSKSPDSYQKILLAICEQAAETNNVYKKHRILKIYHLFVSLLL  1450
1451  KDIKSGLGGAWAFVLRDVIYTLIHYINQRPSCIMDVSLRSFSLCCDLLSQ  1500
1501  VCQTAVTYCKDALENHLHVIVGTLIPLVYEQVEVQKQVLDLLKYLVIDNK  1550
1551  DNENLYITIKLLDPFPDHVVFKDLRITQQKIKYSRGPFSLLEEINHFLSV  1600
1601  SVYDALPLTRLEGLKDLRRQLELHKDQMVDIMRASQDNPQDGIMVKLVVN  1650
1651  LLQLSKMAINHTGEKEVLEAVGSCLGEVGPIDFSTIAIQHSKDASYTKAL  1700
1701  KLFEDKELQWTFIMLTYLNNTLVEDCVKVRSAAVTCLKNILATKTGHSFW  1750
1751  EIYKMTTDPMLAYLQPFRTSRKKFLEVPRFDKENPFEGLDDINLWIPLSE  1800
1801  NHDIWIKTLTCAFLDSGGTKCEILQLLKPMCEVKTDFCQTVLPYLIHDIL  1850
1851  LQDTNESWRNLLSTHVQGFFTSCLRHFSQTSRSTTPANLDSESEHFFRCC  1900
1901  LDKKSQRTMLAVVDYMRRQKRPSSGTIFNDAFWLDLNYLEVAKVAQSCAA  1950
1951  HFTALLYAEIYADKKSMDDQEKRSLAFEEGSQSTTISSLSEKSKEETGIS  2000
2001  LQDLLLEIYRSIGEPDSLYGCGGGKMLQPITRLRTYEHEAMWGKALVTYD  2050
2051  LETAIPSSTRQAGIIQALQNLGLCHILSVYLKGLDYENKDWCPELEELHY  2100
2101  QAAWRNMQWDHCTSVSKEVEGTSYHESLYNALQSLRDREFSTFYESLKYA  2150
2151  RVKEVEEMCKRSLESVYSLYPTLSRLQAIGELESIGELFSRSVTHRQLSE  2200
2201  VYIKWQKHSQLLKDSDFSFQEPIMALRTVILEILMEKEMDNSQRECIKDI  2250
2251  LTKHLVELSILARTFKNTQLPERAIFQIKQYNSVSCGVSEWQLEEAQVFW  2300
2301  AKKEQSLALSILKQMIKKLDASCAANNPSLKLTYTECLRVCGNWLAETCL  2350
2351  ENPAVIMQTYLEKAVEVAGNYDGESSDELRNGKMKAFLSLARFSDTQYQR  2400
2401  IENYMKSSEFENKQALLKRAKEEVGLLREHKIQTNRYTVKVQRELELDEL  2450
2451  ALRALKEDRKRFLCKAVENYINCLLSGEEHDMWVFRLCSLWLENSGVSEV  2500
2501  NGMMKRDGMKIPTYKFLPLMYQLAARMGTKMMGGLGFHEVLNNLISRISM  2550
2551  DHPHHTLFIILALANANRDEFLTKPEVARRSRITKNVPKQSSQLDEDRTE  2600
2601  AANRIICTIRSRRPQMVRSVEALCDAYIILANLDATQWKTQRKGINIPAD  2650
2651  QPITKLKNLEDVVVPTMEIKVDHTGEYGNLVTIQSFKAEFRLAGGVNLPK  2700
2701  IIDCVGSDGKERRQLVKGRDDLRQDAVMQQVFQMCNTLLQRNTETRKRKL  2750
2751  TICTYKVVPLSQRSGVLEWCTGTVPIGEFLVNNEDGAHKRYRPNDFSAFQ  2800
2801  CQKKMMEVQKKSFEEKYEVFMDVCQNFQPVFRYFCMEKFLDPAIWFEKRL  2850
2851  AYTRSVATSSIVGYILGLGDRHVQNILINEQSAELVHIDLGVAFEQGKIL  2900
2901  PTPETVPFRLTRDIVDGMGITGVEGVFRRCCEKTMEVMRNSQETLLTIVE  2950
2951  VLLYDPLFDWTMNPLKALYLQQRPEDETELHPTLNADDQECKRNLSDIDQ  3000
3001  SFNKVAERVLMRLQEKLKGVEEGTVLSVGGQVNLLIQQAIDPKNLSRLFP  3050
3051  GWKAWV                                              3056

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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