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

NucPred

Fetching O42926 from www.uniprot.org...

The NucPred score for your sequence is 0.89 (see score help below)

   1  MLEGLLANFLNRLLGEYIENFDATQLKVAVWNGDVTLRNLQLKRSAFRKL    50
  51  ELPISVQYGLVEELTLKIPWSSLKNKPVEIYIVGIRALASMEENVKSQSE   100
 101  VDPHQVLESKRRQMQLWEASQIGKAETAYDPKTQTFTESLITRMIDNIQI   150
 151  NIRDIHIRFEHLPVSNVVPGGYSFGLLLSEFSIESCNEEWTSTFVETESQ   200
 201  TIYKLCSLRGFGIYSDETAECIDKTDINELMNTFQALITDFQSREKDYII   250
 251  APVTGMAKVTINKLPTPEIPRFLSQISFRGFDVSLNDSQISCGMSLAHEL   300
 301  QDVMSKLAFRKRIVGNVSLDTPLDYLRFIFQKTLHDIQQKHYARSWPAIK   350
 351  AFCEKRRNYIKLYKKKFLAVQLSADESKELDVLELNLDISQLKLFRSLAY   400
 401  QEIKNEGFEPQPPKQQGWGSWMWNSFRGASDENGEVTDDQRKTVIDAIGL   450
 451  NDSIIESAHVGDLQSNTSLFDVRLEVPSGKFSLLKYPKKENVISLELLNF   500
 501  FSQFKCNKKDYSFVANLGSLKMYNDDRNFLYPRETSNKEIETTSPSIFTL   550
 551  SFERSSSDDNDTDTLGINLRALEFFYDPNLLLRVKGFQSSLFANDNGRKL   600
 601  VRLANDAVTDFTSQTVQNLQEYLNEKRKLNVNFQFQTPLLIFPEDCNNPE   650
 651  SFSLFVDAGFISITSQNVETNDKESNSESETLYHLIYDRYRVSLESARLL   700
 701  LGPLNELKNESNKINEEYYLMNELNTEIFIQAQRVPTPQYPRIKVEGNMP   750
 751  CLSFILSDAQFRILNNLASTMLKDGKASDDDDNGDWRPESSESLDSHESE   800
 801  YKLNNTPSEQSVKVSHFFEFNMKLGEVTLILCREDSQTKRNSMVSVNFAK   850
 851  LLLSFNQFEDNSHLRMSINSFHINDMLSKSSRDNNRQLMRCYAPDSVDAE   900
 901  NTPVIVEIKSVKAGENQSETNTTVDFLCANGDFYLAKFSILTLIEFLPSF   950
 951  APPPSNDKQSTPQVSNSAAGKMELNFKIHKIGLRLLENYESKPICIDLLA  1000
1001  LDLSVNSTKGISEVESRVDKIQVMAWDHEKDEIVTLIDSKQDSLFDLKCK  1050
1051  MQEGWFIHSPNPSTDVNIKMGSFTMLCHKQPIEEILNYASNFGHYKAIVQ  1100
1101  SVKYLAETGTHQVQQGTNVNINLYISNPIFQIPLTLENGSSAMVEILPGS  1150
1151  FSLKTPSWLPNLTLNLESKSTTLRTIYYSKDFDEKGNQITVLDDLNISLD  1200
1201  GSIVQAVDSAITNYAIDLHCGISELLIHLSQAQYLILLKLASNLPEMLSI  1250
1251  ANAFSANVDAPSVMTLLSEELYSIDTVNDAISNLSQNSSFDMKFGLHFPK  1300
1301  ISLNLYDGTFITPENSLSPLSNFTLNEIRAEGSYDLKTGASALIKMASLA  1350
1351  IEDVRSEKSRYFSNVIIPSTDTESQLQVSFQYKPDTSSILLEGDIFKSMY  1400
1401  VLSLDHLLSIYYWFGQPLMEKKLESPDDVQSLQAESSVSTPAVTAASEKS  1450
1451  ISLSVRFDIRNTSLVFVADASKSTSQAIVLRTDQISLVKQLSYSLTVQKM  1500
1501  GMFVTRMDKLDDGIQILDDFDIGFGLVQDCSENKSFSATLDLDRLLFRIS  1550
1551  VYDLLLLQSIAQASVSVISSYKDKSSSISENMNSGDYGQQILNASNIAAV  1600
1601  QQKAEQTATTLLTVLGHDSLISEEFTINSAGIQLILISDAHCLPVFDFTI  1650
1651  ENFNVLVKDWSTNLSATTSLTLHCNAFNFAKSHWEPVIEPWTFSTTAIMK  1700
1701  DGMHEVNINSDDIAQISLTPMMVTDVHRLIKFYLTNQENNIEKRPEGYPY  1750
1751  VILNQTGYNLSIQYGNLNSSEMQSLSLPSGKCVPCRFESKEVLTSRMSSK  1800
1801  VQDVATKVRVSFDSTWYPVDEVSVHQEGSFLYELKPRIDQRTFLLVTVVL  1850
1851  LESNIKQIILSSPYSIVNRTKEVIEVVCNDRSGHRQSSVIKIDPNETGYV  1900
1901  PLDLACLYPLRIRPVSKLGFLWSNQIVDWHSLNKSPLQYLTCESTSTSWK  1950
1951  HNLLVFARNLMDGSLQNDYPFLQLNILPTLQIENLLPYEINLRIIERSSG  2000
2001  NDWRSSLSPGDSLPILHTDSKSFLLMGINVPDLDLQPVDLPIIYTPISSG  2050
2051  QDVQTSALLTASDKQDVVKLILKYEKLPGTNYVSKVMIYPPYVIFNHTDL  2100
2101  SIQVTSSSPNSIRYTIPSGSYSNDIKPYFYSFDESGRKNRAMISIDNGTS  2150
2151  WSADIGFDTLGSSSQVEVRKTNESDVCLLGMSISESSGKFCLTKSVTFTP  2200
2201  RFVFKNHLDCTVSLREFGSSKVLHLPSNELIPMMYFSNPQEIALLLSLPS  2250
2251  SNNHWTSPFLAQNVGIVHLKAFEFDDDDNNMSTTLLRLCVTLEDATFFVT  2300
2301  ITKEDKAWPFRLKNCTSREICYEQKRPDPESVDSRFLQGSRSMKYALNPG  2350
2351  EEANYSWDFPILKSKLLQVEVGKAIHDLDISSVGQLEPWHPTELDQKIRI  2400
2401  HPEVKVDSLTSLVTFNEIDLSKPKLPSRTNSNVKGSIVEQKFKLVLQLKG  2450
2451  FGISLIDKKYEEFAYATLKNFTFRFDDSKDLNTFGMSLGWLQIDNQMLDS  2500
2501  VYPIALFPTMITQEVKQDDPQLLQLRFSVLKDSSFNILYIKYASLLLQEL  2550
2551  SLEVEDRLVLTLLQLLYPSSDVSKDSASLSKNAFADKFEIPDLDADVYRS  2600
2601  NVFFETLHLQPTRLNISFETSYESDQPAVKSSNPTLDFMTGILISTLGNI  2650
2651  HDAPVQLNSILLENARGTLSEMANRVASHYKQQVGYQIYKIAGRADFLGN  2700
2701  PVGLFNNVASGVFDMFYEPYQGFLLQDSQSFGDSFARGTSSFMRKTIYGV  2750
2751  SDSVSKITGTISKGLSTMTMDPKYQNSRRRFRSRNRPKEAVYGVTAGANS  2800
2801  FYDSMSSGFKGLKKPFTDPKNNSAGKFLKGFGKGMLGLATKPAIGLLDMT  2850
2851  SNVSEGIRNSTDVRTNPEIDKVRVPRYVEFGGLIVPFKPYESLGKYMLSC  2900
2901  LDDGKYAFDEYLYHAEIQNVDILYISTKHFIITGSNYIVKIAVPVKQISG  2950
2951  LRVSEHDLNSSCLFTFAVFWQRCESLFDCEYLSTPNLELFVKEKKKPIMS  3000
3001  ITMSDSSAYGEELMRERFEHLLKAYEKMALMVAEQEEFNAKIEDMALKLL  3050
3051  SEKYDNEAYQAELFYRLSNCVEKVLHNKISITDLKTEYEEILEQTLKKEC  3100
3101  KAYERSCIENVKLKKRTEQATAYYASSSSEP                     3131

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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