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

NucPred

Fetching Q6PQD5 from www.uniprot.org...

The NucPred score for your sequence is 0.97 (see score help below)

   1  MSLALNDLLICCRQLEHDRATERRKAVENFRHLIQDPETVQHLDQHSDSK    50
  51  QGKYLNWDAAFRFLQKYIQKETECLRTAKQNVSASTQATRQKKMQEISSL   100
 101  VKYFIKCANKRAPRLKCQELLNYIMDTVRDSSNNPIYGADYSNILLKDIL   150
 151  SVRKYWCEISQQQWRELFLIYFTLYLKPSQDINRLLVARIIQAVTKGCCS   200
 201  QTDGLNSEFLDFFTKAIQNARQEKSSPGLNHILAAYVIFLKTLAANFRIR   250
 251  VCELGDKILPTLLYIWTQHRLNDSLKEVIVELFQLQVYMHHPKGAKTQEK   300
 301  GAYESAKWKSILYNLYDLLVNEISRIGSRGKYSSGSRNIAVKENLIELMA   350
 351  DICHQVFNEDTRSLEISQSYTTTQREFSDYNAPCKKRKIELGWGVIKDHL   400
 401  QKSQNDFDVVPWLQIATQLISKYPASLPNCELSPLLMILYQLLPQQRRGE   450
 451  RTPYVLRCLMEVALCQGKKPNLESSHKSDLLKIWIKIWSITFRGISSEQI   500
 501  QAENFGLLGAIIQGSLVEVDREFWKLFTGSACKPSCPTVCCLTLALKTCV   550
 551  VPETVETGMENICDGNRKFSLKESIMKWLLFCQLEDDFEDRIELPPILHS   600
 601  NFPHLALEKILVSLIMKNCKAAMNFFQSVPECEQHQKDTEEPSLLEVEEL   650
 651  FLQTTFDKMDFLTVVQECTIEKHQSSVGFSFHQNLKESLDRYLLGLSEQL   700
 701  LNNYLPETSDSETLVRCSSLLVGVLGCYCYVGVIAEEEAYTSELFQKAKS   750
 751  LMQCAGESITLFKSKTNEESRIISLRNMMHLCTNCLYKCAKRSPNKIASG   800
 801  FFLRLLTSKLMHDIADVCRSLAFIIKKPFDCREVESMEDDTDKNLMEMND   850
 851  QSSMSLFNDNPASSVIDANESGESQITMGAMNPLAEEHLSKQDLLVLDML   900
 901  RFLCMCITIAQSNTMSFRAADIRRKLLMLIDSDRLDPTKSLHLHMYLVLL   950
 951  KELPGEEYPLPMEDVVELLKPLSSVCSLYRRDQDVCKTILNHVLHIVPNL  1000
1001  CRENVDAESTRDAQGQFLTVIGAFWHLTKEGKCTFSVRLALVKCLKTLLE  1050
1051  ADPYSRWAILNVMEKDFPVNEVFPQFLADNHHQVCMLAAGLINRLFQHMK  1100
1101  QGDSSTIMRALPLKLQQTAFENAYLKAQERIRQVKSQGGENRELLDEICN  1150
1151  RKAVLLTMIAVVLCCSPVCEKQALFALCKSVKENGLEPHLIKKVLEKVSE  1200
1201  TFGYRHLEDFMASHLDYLVLEWLHLQDAEYSLSSFPFILLNYTNIEDFYR  1250
1251  SCYKVLIPHLVMRCHFDEVKSIANQIQGDWKSLLTDCFPKILVNILPYFA  1300
1301  YEDTGDRGMAQQRETASKVYDMLKDENLLGKQIDQLFINNLPEIVVELLM  1350
1351  TLHEPATSDASQSTDPCDFSGDLDPRPNPPHFPSHVIKATFAYISNCHKT  1400
1401  KLKSILEVLSKSPDSYQKILLAICEQAAETNNVYKKHRILKIYHLFVSLL  1450
1451  LKDMKSGLGGAWAFVLRDVIYTLIHYINKRPSRFMDVSLRSFSLCCDLLS  1500
1501  RVCHTAVTYSKDALESHLHVIVGTLIPLVDGQMEVQKQVLDLLKYLVIDN  1550
1551  KDNENLYVMIKLLDPFPDNAVFKDLRITQQEIKYSKGPFSLLEEINHFLS  1600
1601  VSVYDALPLTRLEGLKDLRRQLAQHKDQMMDLMRASQDNPQDGIVVKLVV  1650
1651  SLLQLSKMAVNHTGEREVLEAVGRCLGEVGPIDFSTIAIQHSKDMPYTKA  1700
1701  LELFEDKEHHWTLMMLTYLNSTLVEDCVKVRSAAVTCLKSILATKTGHGF  1750
1751  WEIFKTTADPMLTYLLPFRTSRKKFLEVPRLNKESPLEGLDDISLWIPQS  1800
1801  ENHDIWIKTLTCALLDSGGINSEVLQLLKPMCEVKTDFCQTVLPYLIHDI  1850
1851  LLQDTNESWRSLLSTHIQGFFTNCFRHSSQTSRSTTPANMDSESEHVFRC  1900
1901  HLDKKSQRTMLAVVDYMRRQKRSSSGTVFDDAFWLELNYLEVAKVAQSCA  1950
1951  AHFTALLYAEIYADKKNMDDQEKRSPTFEEGSQSTTISSLSEKSKEETGI  2000
2001  SLQDLLLEIYRSIGEPDSLYGCGGGKMLQPLTRLRTYEHEAMWGKALVTY  2050
2051  DLETAISSSTRQAGIIQALQNLGLCHILSVYLKGLDHENKEQCAELQELH  2100
2101  YQVAWRNMQWDSCVSVNKGMEGTSYHESLYNALQSLRDREFSTFYESLKY  2150
2151  ARVKEVEELCKGSLESVYSLYPTLSRLQAIGELENIGELFSRSVTDRQPS  2200
2201  EVYNKWWKHSQLLKDSDFSFQEPIMALRTVILEILMEKEMENSQRECLKD  2250
2251  ILTKHLVELSLLARTFQNTQLPERAIFQIKQYNSANCGVSEWQLEEAQVF  2300
2301  WAKKEQSLALSILKQMIKKLDASCTENDPRLKLIHIECLRVCGTWLAETC  2350
2351  LENPAVIMQTYLEKAVELAGNYDGESNDELRNGKMKAFLSLARFSDTQYQ  2400
2401  RIENYMKSSEFENKQALLKRAKEEVGLLREHKIQTNRYTIKVQRELELDE  2450
2451  GALRALKKDRKRFLCKAVENYINCLLSGEGHDMWIFRLCSLWLENSGVSE  2500
2501  VNGMMKRDGMKIPSYKFLPLMYQLAARMGTKMMGGLGFHDVLNSLISRIS  2550
2551  VDHPHHTLFIILALANANKDEFLTKPEAARSSRITKNTPKESSQLDEDRT  2600
2601  EAANKVICTLRNRRRQMVRSVEALCDAYIILANLDATQWRTQRKGIRIPA  2650
2651  DQPITKLKNLEDVVVPTMEIKVDPTGEYGNMVTIQSFKPEFRLAGGLNLP  2700
2701  KIIDCVGSDGKERRQLVKGRDDLRQDAVMQQVFQMCNTLLQRNTETRKRK  2750
2751  LTICTYKVVPLSQRSGVLEWCTGTVPIGEYLVNNDTGAHKRYRPKDFSPV  2800
2801  QCQKKMMEAQNKSFEEKYEIFMNICQNFQPVFRYFCMEKFLDPAVWFERR  2850
2851  LAYTQSVATSSIVGYILGLGDRHVQNILINEQSAELVHIDLGVAFEQGKI  2900
2901  LPTPETVPFRLTRDIVDGMGITGVEGVFRRCCEKTMEVMRNSQETLLTIV  2950
2951  EVLLYDPLFDWTMNPLKALYLQQRPEDESELHSTPRADDQECKRNLSDTD  3000
3001  QSFNKVAERVLMRLQEKLKGVEEGTVLSVGGQVNFLIQQAMDPKNLSKLF  3050
3051  SGWKAWV                                             3057

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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