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

NucPred

Fetching Q09624 from www.uniprot.org...

The NucPred score for your sequence is 0.77 (see score help below)

   1  MKKSNFFVLLLLAISAIQIDGLHYQLLDGIATFRLDNDDTTIGGVPRNSQ    50
  51  GVVKIKLSCGLNRLSVENKVTEVSSLELIHNCIQTETRLVGLFLNSTWIT   100
 101  LNEVNDDDEISIAVEAKYEVCYDDGIDRCDGSLWWLQVGGNEMALLGYRE   150
 151  KCESGEINEEYARRMCKRPYRSEKSTAISDSQGVYYDGQVLKGVRAKQFS   200
 201  MRTSGSPTLRRMKRDAGDNTCDYTIESTSTSTTTPTTTTVTSTVTSTTTV   250
 251  PTSTSTVTTAMSTSTSTPSTSTTIESTSTTFTSTASTSTSSTSTTQQSSS   300
 301  TITSSPSSTTLSTSIPTTTTPEITSTLSSLPDNAICSYLDETTTSTTFTT   350
 351  TMLTSTTTEEPSTSTTTTEVTSTSSTVTTTEPTTTLTTSTASTSTTEPST   400
 401  STVTTSPSTSPVTSTVTSSSSSSTTVTTPTSTESTSTSPSSTVTTSTTAP   450
 451  STSTTGPSSSSSTPSSTASSSVSSTASSTQSSTSTQQSSTTTKSETTTSS   500
 501  DGTNPDFYFVEKATTTFYDSTSVNLTLNSGLGIIGYQTSIECTSPTSSNY   550
 551  VSTTKDGACFTKSVSMPRLGGTYPASTFVGPGNYTFRATMTTDDKKVYYT   600
 601  YANVYIQEYSSTTIESESSTSAVASSTSSTPSTPSSTLSTSTVTEPSSTR   650
 651  SSDSTTTSAGSTTTLQESTTTSEESTTDSSTTTISDTSTTSSSPSSTTAD   700
 701  STSTLSVDQFDFILDSGLSWNETRHNEDSINIVPLPTNAITPTERSQTFE   750
 751  CRNVSTEPFLIIKESTCLNYSNTVLNATYSSNIPIQPIETFLVGIGTYEF   800
 801  RINMTDLTTMQVVSHIFTLNVVADSTSTSEVTSTTSTGSSSESSAISTTS   850
 851  GIESTSTLEASTTDASQDSSTSTSDSGTTSDSTTIDSSNSTPSTSDSSGL   900
 901  SQTPSDSSSASDSMRTTTVDPDASTETPYDFVLENLTWNETVYYSENPFY   950
 951  ITPIPNKEPGALTTAMTCQCRNDSSQPFVLLKESNCLTEFGKNGAYSASV  1000
1001  SFNPMTSFVPATGTYEFLINVTNRASGESASHIFTMNVVLPTTTTETPPT  1050
1051  TVSSSDDAGGKTGGTGATGGTGGTGSGGSATTLSTGDAVRSTTSGSGSGQ  1100
1101  SSTGSGAGGSGTTASGSGSGGSSGTGSDGVNSGKTTALNGDGTGSGTATT  1150
1151  PGSHLGDGGSTSGSGSDSNGSSGVSTKSSSGSDTSGSSDSSGANGAFSAT  1200
1201  AQPSTRTTKTRSSLATVSPISAAEQAIIDAQKADVMNQLAGIMDGSASNN  1250
1251  SLNTSSSLLNQISSLPAADLVEVAQSLLSNTLKIPGVGNMSSVDVLKTLQ  1300
1301  DNIATTNSELADEMAKVITKLANVNMTSAQSLNSVLSSLDLALKGSTVYT  1350
1351  LGVSSTKSKDGTYAVIFGYVIASGYTLVSPRCTLSIYGSTIYLTGDTRAS  1400
1401  YKQLDGDTVTADTMLAAAIGIQGMFATNGRTVQVEQDKIDDKRSLVSGNI  1450
1451  MATMSGVGDVQSGEYSYNDMYVTAWNVTYDNSTVGSTSQKNTSFSFNIPV  1500
1501  SEVQYILLIESGTMIKLHSTQNIVSRGLVVTASYGGVTYTITCTNGTGKF  1550
1551  VEVDTDNAIFSYNADSFTVVASDGSSASTVKKLIQMPIVIENVNLALFNQ  1600
1601  TTSPLVFSNAGSYSMRMVLSPQDIGIPAVSALSQTVSISTLSPTASYTKD  1650
1651  DLQSLIKEQTLVTVSGTLFFSKASSIDVSGYSFFVDSTALYLSNSVTTLV  1700
1701  ISSPTYNIVSLALGGYGIQITAGTYTSGSQTHTVTLMEFSDTQKMRIDGG  1750
1751  LIIRNGTNGYVIQNGQVSTEGDVSGTKIDIVPQSLMNQESQQQIETILSN  1800
1801  TQDFLTNNGMTMTDAEINDTSNSLLSIAGSLTSALKIALDNPLSSDLAAN  1850
1851  LKYATDNYDSLYNVLPSDPDNIVYVEEMTSEEWAAYVTKMFQKNIAKNLA  1900
1901  NQLASTLDTLENTLAARAIATGNLPYDYSNSVDGTGMVIVIDDASNIVGK  1950
1951  TQNCEEWAFKLPSPASTLNTAEITDKTLIQVGLVCYATNPRTYVDNFDML  2000
2001  ITSGALEAHIKDENQIIIPITGTTAPIYVNGRGSEDDAVLTLMQQGDFAS  2050
2051  YQILDLHAFRTTNWNNSLQVEIIASQDYEIPNNDDTYMFSSFQSLPGPLE  2100
2101  SNHEWIFDLNTLNKTSNYFVTAGNLINNTGLFFIGIGKRNSSTNTGNSSD  2150
2151  IVNYGQYDSMQWSFARSVPMDYQVAAVSKGCYFYQKTSDVFNSEGMYPSD  2200
2201  GQGMQFVNCSTDHLTMFSVGAFNPTIDADFSYNYNVNEIEKNVKVMIAAV  2250
2251  FMLVVYGCLTINAIICQRKDASRGRLRFLKDNEPHDGYMYVIAVETGYRM  2300
2301  FATTDSTICFNLSGNEGDQIFRSFRSEEDGNWEFPFSWGTTDRFVMTTAF  2350
2351  PLGELEYMRLWLDDAGLDHRESWYCNRIIVKDLQTQDIYYFPFNNWLGTK  2400
2401  NGDGETERLARVEYKRRFLDESMSMHMLAQTISWFAMFTGGGNRLRDRVS  2450
2451  RQDYSVSIIFSLVVVSMISITILKSDNSIISDSKSVSEFTFTIKDIAFGV  2500
2501  GFGVLITFLNSLHILLCTKCRSHSEHYYYKKRKREDPEFKDNSGSWPMFM  2550
2551  AGMARTIIVFPVLMGLIYISGAGMSLMDDLANSFYIRFLISLILWAVVFE  2600
2601  PIKGLIWAFLILKTRKSHKIINKLEEALLRAKPAETFLRNPYGKIEKGLG  2650
2651  TEIADVTKLRDTENRKMRDEQLFITIRDMLCFFASLYIMVMLTYYCKDRH  2700
2701  GYWYQLEMSTILNINQKNYGDNTFMSIQHADDFWDWARESLATALLASWY  2750
2751  DGNPAYGMRAYMNDKVSRSMGIGTIRQVRTKKSAECTMFKQFQGYINDCG  2800
2801  EELTSKNEEKTLYMQAGWTELESENGTDASDEYTYKTSEELSTETVSGLL  2850
2851  YSYSGGGYTISMSGTQAEIITLFNKLDSERWIDDHTRAVIIEFSAYNAQI  2900
2901  NYFSVVQLLVEIPKSGIYLPNSWVESVRLIKSEGSDGTVVKYYEMLYIFF  2950
2951  SVLIFVKEIVFYLYGRYKVITTMKPTRNPFKIVYQLALGNFSPWNFMDLI  3000
3001  VGALAVASVLAYTIRQRTTNRAMEDFNANNGNSYINLTEQRNWEIVFSYC  3050
3051  LAGAVFFTSCKMIRILRFNRRIGVLAATLDNALGAIVSFGIAFLFFSMTF  3100
3101  NSVLYAVLGNKMGGYRSLMATFQTALAGMLGKLDVTSIQPISQFAFVVIM  3150
3151  LYMIAGSKLVLQLYVTIIMFEFEEIRNDSEKQTNDYEIIDHIKYKTKRRL  3200
3201  GLLEPKDFAPVSIADTQKDFRLFHSAVAKVNLLHHRATRMLQTQGQYQNQ  3250
3251  TVINYTLSYDPVSAIHETGPKRFQKWRLNDVEKD                  3284

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.)

Go back to the NucPred Home Page.