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

NucPred

Fetching Q01397 from www.uniprot.org...

The NucPred score for your sequence is 0.85 (see score help below)

   1  MSELGVAVGQKIELADGSGRTAFVRYVGETAFAPGTWVGIELDEPSGKND    50
  51  GSVQGERYFNCEMGYGMFVRPTTFNVIAQPPPPPPPSTFRRSVTTRPTSL   100
 101  NASTTRRPAPVDSGLAKRMSLNAPSPSPGPRPSRTSSTSITRSPTRSPTK   150
 151  QLATASSSGNPSRSGTPSTTTKPAGPTTRTRPSLSTSRHSMGPPPTPTTR   200
 201  TTRKPSVSSVGTRPSIGATRPVGGRASMSARSSTNRLSDPRESTGSVSSV   250
 251  GKSGFKRGSASPRSSDEELSASPVPASPVHQKTAALEKLTAPGAGNGGGG   300
 301  ASPGATSPNLKATTITPRSSITNTATMNKEIEDLKAKLKVLEKKRMEDRE   350
 351  KLNSLEKVKAERDKFERIIQTLQIKYQPQQQEIQDLKRQLKEAENRLYNV   400
 401  EELQAEHDTAMELATLDREMAEETAEVLKVELDALKQKNEELELEVEVLR   450
 451  EENSEFTNGMSPEERASTGWLQMERNNERLREALIRLRDITQQQEEELKD   500
 501  QIKSMEEDLREFETIKEQHTTAKEKLAQTEAIVEDLREQLNNALGAEEII   550
 551  ESLTEQTMNQSEEIKELRAVIDDLESLKEINDELEINHVQNEKEMQEEID   600
 601  LKDSIIAEQFRQANLQRESLEDMEYTLSRFRELVTSLQSDLEDMRASHAV   650
 651  TENESEQLNSRSRAMLDLNMKLQISASKAQVKTIDLELRRMEAQEAEQHL   700
 701  EIVKLFLPDTYQSDRDSVLALLRFKRLAFKANLLNGFIKERVNGQPHPGH   750
 751  EDDIFDGCDAIDKLTWVSAMCDRFVNAISHCSLEQFSRYEGALYELEPVE   800
 801  RALNGWIDGLRRDDLKEKQCADELKRTIALMTHLGEVHISNDLESFADDV   850
 851  HMKALLMQSHLESAAISVNSLKAMVQRVIPSSGEDDELAQYFSKRAEAVV   900
 901  SQTRSAKVIAGKTVRALEDLKTRSLSLTPDTLEAFEQSETATRDLANMAR   950
 951  QIGLDLHAFLHEEGRTEPYNYMEVQSCIQRSAQASSPTASSPESDLFNTY  1000
1001  LSYLRNATSTISDLASLASDLAQTQEFDRTPAPWILRSAELKAAKTVAPD  1050
1051  INDELRRLRDDIAEARRSIAVREEMLSTAQVKIETLESRMRDANAKAARI  1100
1101  VDLEADLQAAKKEAAQLQEDMEKQDRELKALESDRDKWKKIASESRVVVA  1150
1151  DGSGVGVDNKASAERAVATAREMDALKKEIEALQAAVRYLREDSRRARLK  1200
1201  EQGDYEWLAEPLVKKKPSVQEQRKQLVKKEGKAVLGELVKLVSSAKVFDL  1250
1251  GSLPEKAEDRLKWRPAKTTPGWWVAKQMEDWEALKEWEGSVKGRVRELGG  1300
1301  VPGSKKAEREEEAKRMVRRTAAAKLQIRLPGMEGKVGHGGGGGSGRRVQI  1350
1351  VGSREWESLQGRLAVVV                                   1367

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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