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

NucPred

Fetching Q92628 from www.uniprot.org...

The NucPred score for your sequence is 0.68 (see score help below)

   1  MYPICTVVVDGLPSESSSSSYPGPVSVSEMSLLHALGPVQTWLGQELEKC    50
  51  GIDAMIYTRYVLSLLLHDSYDYDLQEQENDIFLGWEKGAYKKWGKSKKKC   100
 101  SDLTLEEMKKQAAVQCLRSASDESSGIETLVEELCSRLKDLQSKQEEKIH   150
 151  KKLEGSPSPEAELSPPAKDQVEMYYEAFPPLSEKPVCLQEIMTVWNKSKV   200
 201  CSYSSSSSSSTAPPASTDTSSPKDCNSESEVTKERSSEVPTTVHEKTQSK   250
 251  SKNEKENKFSNGTIEEKPALYKKQIRHKPEGKIRPRSWSSGSSEAGSSSS   300
 301  GNQGELKASMKYVKVRHKAREIRNKKGRNGQSRLSLKHGEKAERNIHTGS   350
 351  SSSSSSGSVKQLCKRGKRPLKEIGRKDPGSTEGKDLYMENRKDTEYKEEP   400
 401  LWYTEPIAEYFVPLSRKSKLETTYRNRQDTSDLTSEAVEELSESVHGLCI   450
 451  SNNNLHKTYLAAGTFIDGHFVEMPAVINEDIDLTGTSLCSLPEDNKYLDD   500
 501  IHLSELTHFYEVDIDQSMLDPGASETMQGESRILNMIRQKSKENTDFEAE   550
 551  CCIVLDGMELQGERAIWTDSTSSVGAEGLFLQDLGNLAQFWECCSSSSGD   600
 601  ADGESFGGDSPVRLSPILDSTVLNSHLLAGNQELFSDINEGSGINSCFSV   650
 651  FEVQCSNSVLPFSFETLNLGNENTDSSANMLGKTQSRLLIWTKNSAFEEN   700
 701  EHCSNLSTRTCSPWSHSEETRSDNETLNIQFEESTQFNAEDINYVVPRVS   750
 751  SNYVDEELLDFLQDETCQQNSRTLGEIPTLVFKKTSKLESVCGIQLEQKT   800
 801  ENKNFETTQVCNESPHGDGYSSGVIKDIWTKMADTNSVATVEIERTDAEL   850
 851  FSADVNNYCCCLDAEAELETLQEPDKAVRRSEYHLWEGQKESLEKRAFAS   900
 901  SELSNVDGGDYTTPSKPWDVAQDKENTFILGGVYGELKTFNSDGEWAVVP   950
 951  PSHTKGSLLQCAASDVVTIAGTDVFMTPGNSFAPGHRQLWKPFVSFEQND  1000
1001  QPKSGENGLNKGFSFIFHEDLLGACGNFQVEDPGLEYSFSSFDLSNPFSQ  1050
1051  VLHVECSFEPEGIASFSPSFKPKSILCSDSDSEVFHPRICGVDRTQYRAI  1100
1101  RISPRTHFRPISASELSPGGGSESEFESEKDEANIPIPSQVDIFEDPQAD  1150
1151  LKPLEEDAEKEGHYYGKSELESGKFLPRLKKSGMEKSAQTSLDSQEESTG  1200
1201  ILSVGKQNQCLECSMNESLEIDLESSEANCKIMAQCEEEINNFCGCKAGC  1250
1251  QFPAYEDNPVSSGQLEEFPVLNTDIQGMNRSQEKQTWWEKALYSPLFPAS  1300
1301  ECEECYTNAKGESGLEEYPDAKETPSNEERLLDFNRVSSVYEARCTGERD  1350
1351  SGAKSDGFRGKMCSSASSTSEETGSEGGGEWVGPSEEELFSRTHL       1395

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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