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

NucPred

Fetching Q9Y2A5 from www.uniprot.org...

The NucPred score for your sequence is 0.96 (see score help below)

   1  MTDRFWDQWYLWYLRLLRLLDRGSFRNDGLKASDVLPILKEKVAFVSGGR    50
  51  DKRGGPILTFPARSNHDRIRQEDLRKLVTYLASVPSEDVCKRGFTVIIDM   100
 101  RGSKWDLIKPLLKTLQEAFPAEIHVALIIKPDNFWQKQKTNFGSSKFIFE   150
 151  TSMVSVEGLTKLVDPSQLTEEFDGSLDYNHEEWIELRLSLEEFFNSAVHL   200
 201  LSRLEDLQEMLARKEFPVDVEGSRRLIDEHTQLKKKVLKAPVEELDREGQ   250
 251  RLLQCIRCSDGFSGRNCIPGSADFQSLVPKITSLLDKLHSTRQHLHQMWH   300
 301  VRKLKLDQCFQLRLFEQDAEKMFDWISHNKELFLQSHTEIGVSYQYALDL   350
 351  QTQHNHFAMNSMNAYVNINRIMSVASRLSEAGHYASQQIKQISTQLDQEW   400
 401  KSFAAALDERSTILAMSAVFHQKAEQFLSGVDAWCKMCSEGGLPSEMQDL   450
 451  ELAIHHHQTLYEQVTQAYTEVSQDGKALLDVLQRPLSPGNSESLTATANY   500
 501  SKAVHQVLDVVHEVLHHQRRLESIWQHRKVRLHQRLQLCVFQQDVQQVLD   550
 551  WIENHGEAFLSKHTGVGKSLHRARALQKRHDDFEEVAQNTYTNADKLLEA   600
 601  AEQLAQTGECDPEEIYKAARHLEVRIQDFVRRVEQRKLLLDMSVSFHTHT   650
 651  KELWTWMEDLQKEMLEDVCADSVDAVQELIKQFQQQQTATLDATLNVIKE   700
 701  GEDLIQQLRSAPPSLGEPSEARDSAVSNNKTPHSSSISHIESVLQQLDDA   750
 751  QVQMEELFHERKIKLDIFLQLRIFEQYTIEVTAELDAWNEDLLRQMNDFN   800
 801  TEDLTLAEQRLQRHTERKLAMNNMTFEVIQQGQDLHQYITEVQASGIELI   850
 851  CEKDIDLAAQVQELLEFLHEKQHELELNAEQTHKRLEQCLQLRHLQAEVK   900
 901  QVLGWIRNGESMLNASLVNASSLSEAEQLQREHEQFQLAIESLFHATSLQ   950
 951  KTHQSALQVQQKAEVLLQAGHYDADAIRECAEKVALHWQQLMLKMEDRLK  1000
1001  LVNASVAFYKTSEQVCSVLESLEQEYRRDEDWCGGRDKLGPAAEIDHVIP  1050
1051  LISKHLEQKEAFLKACTLARRNAEVFLKYIHRNNVSMPSVASHTRGPEQQ  1100
1101  VKAILSELLQRENRVLHFWTLKKRRLDQCQQYVVFERSAKQALDWIQETG  1150
1151  EFYLSTHTSTGETTEETQELLKEYGEFRVPAKQTKEKVKLLIQLADSFVE  1200
1201  KGHIHATEIRKWVTTVDKHYRDFSLRMGKYRYSLEKALGVNTEDNKDLEL  1250
1251  DIIPASLSDREVKLRDANHEVNEEKRKSARKKEFIMAELLQTEKAYVRDL  1300
1301  HECLETYLWEMTSGVEEIPPGILNKEHIIFGNIQEIYDFHNNIFLKELEK  1350
1351  YEQLPEDVGHCFVTWADKFQMYVTYCKNKPDSNQLILEHAGTFFDEIQQR  1400
1401  HGLANSISSYLIKPVQRITKYQLLLKELLTCCEEGKGELKDGLEVMLSVP  1450
1451  KKANDAMHVSMLEGFDENLDVQGELILQDAFQVWDPKSLIRKGRERHLFL  1500
1501  FEISLVFSKEIKDSSGHTKYVYKNKLLTSELGVTEHVEGDPCKFALWSGR  1550
1551  TPSSDNKTVLKASNIETKQEWIKNIREVIQERIIHLKGALKEPLQLPKTP  1600
1601  AKQRNNSKRDGVEDIDSQGDGSSQPDTISIASRTSQNTVDSDKLSGGCEL  1650
1651  TVVLQDFSAGHSSELTIQVGQTVELLERPSERPGWCLVRTTERSPPLEGL  1700
1701  VPSSALCISHSRSSVEMDCFFPLVKDAYSHSSSENGGKSESVANLQAQPS  1750
1751  LNSIHSSPGPKRSTNTLKKWLTSPVRRLNSGKADGNIKKQKKVRDGRKSF  1800
1801  DLGSPKPGDETTPQGDSADEKSKKGWGEDEPDEESHTPLPPPMKIFDNDP  1850
1851  TQDEMSSSLLAARQASTEVPTAADLVNAIEKLVKNKLSLEGSSYRGSLKD  1900
1901  PAGCLNEGMAPPTPPKNPEEEQKAKALRGRMFVLNELVQTEKDYVKDLGI  1950
1951  VVEGFMKRIEEKGVPEDMRGKDKIVFGNIHQIYDWHKDFFLAELEKCIQE  2000
2001  QDRLAQLFIKHERKLHIYVWYCQNKPRSEYIVAEYDAYFEEVKQEINQRL  2050
2051  TLSDFLIKPIQRITKYQLLLKDFLRYSEKAGLECSDIEKAVELMCLVPKR  2100
2101  CNDMMNLGRLQGFEGTLTAQGKLLQQDTFYVIELDAGMQSRTKERRVFLF  2150
2151  EQIVIFSELLRKGSLTPGYMFKRSIKMNYLVLEENVDNDPCKFALMNRET  2200
2201  SERVVLQAANADIQQAWVQDINQVLETQRDFLNALQSPIEYQRKERSTAV  2250
2251  MRSQPARLPQASPRPYSSVPAGSEKPPKGSSYNPPLPPLKISTSNGSPGF  2300
2301  EYHQPGDKFEASKQNDLGGCNGTSSMAVIKDYYALKENEICVSQGEVVQV  2350
2351  LAVNQQNMCLVYQPASDHSPAAEGWVPGSILAPLTKATAAESSDGSIKKS  2400
2401  CSWHTLRMRKRAEVENTGKNEATGPRKPKDILGNKVSVKETNSSEESECD  2450
2451  DLDPNTSMEILNPNFIQEVAPEFLVPLVDVTCLLGDTVILQCKVCGRPKP  2500
2501  TITWKGPDQNILDTDNSSATYTVSSCDSGEITLKICNLMPQDSGIYTCIA  2550
2551  TNDHGTTSTSATVKVQGVPAAPNRPIAQERSCTSVILRWLPPSSTGNCTI  2600
2601  SGYTVEYREEGSQIWQQSVASTLDTYLVIEDLSPGCPYQFRVSASNPWGI  2650
2651  SLPSEPSEFVRLPEYDAAADGATISWKENFDSAYTELNEIGRGRFSIVKK  2700
2701  CIHKATRKDVAVKFVSKKMKKKEQAAHEAALLQHLQHPQYITLHDTYESP  2750
2751  TSYILILELMDDGRLLDYLMNHDELMEEKVAFYIRDIMEALQYLHNCRVA  2800
2801  HLDIKPENLLIDLRIPVPRVKLIDLEDAVQISGHFHIHHLLGNPEFAAPE  2850
2851  VIQGIPVSLGTDIWSIGVLTYVMLSGVSPFLDESKEETCINVCRVDFSFP  2900
2901  HEYFCGVSNAARDFINVILQEDFRRRPTAATCLQHPWLQPHNGSYSKIPL  2950
2951  DTSRLACFIERRKHQNDVRPIPNVKSYIVNRVNQGT                2986

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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