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

NucPred

Fetching P81182 from www.uniprot.org...

The NucPred score for your sequence is 0.61 (see score help below)

   1  MSFDLTTPFDVIKTVSLSARYSWTTSQKGATLNITYNDKNFVLSSSLQLS    50
  51  TRASNITFQATTPFEGFQNSFIEIKYDIDNREELLASRVSVDDHSYSFVV   100
 101  GGYIEDKLAVFKWNLNSPLTGWTDAKFVAKIDLSSENKNLEISLEKEGDL   150
 151  KAIAVSGKFIGSTLDFNLRTPFRGLNNFNVFGSLNRSKRSLEMRMMNDAG   200
 201  QASLAGNFNSLRFNMKTPFERAEQISWEVTKTGEGSYKAEWRRNDNYATF   250
 251  TIEKDVSKQSFDLNIKSEFRGWEILALTGRLDQETKQAYLSGAINEQKIT   300
 301  VTGSGSITNKIKFSMTIETPYENYRQVKAQLNYAKRKNAIKLEASSSSSD   350
 351  FHLLWSRSGSGLEAHLIVPNSRQNTEISINLTPTQGKITITSRFEPIRDY   400
 401  LQEYHVNLGQNEITADHIIKLNGHEVFKMDFERNAPEQKVHLEIHTHVAE   450
 451  RHTTIHFHREGFSKLNFLFKREVPQYGEKHFKVDITGSGALPQKGALDIV   500
 501  VENTFREPAKTINARVEVDRTGARKKIMLEVSPRQSRVYIFNLEYIADLE   550
 551  SPQHGDFTLKITTPNNSPWQNISGNWNVEDPNDATITFTVGNVTYNAKGK   600
 601  LTLRESTMILSSTDPSAENIYLQWKFERNGDTKDYFLKLGRKSRYGMLKL   650
 651  TGTITDIAHVDIEGGFKAGPFMPNEFLFTSMWGKSNGVVTGEGTFDYGNY   700
 701  HGSHRLVKFERNAERKSASFEWSATSNIPQYNSVSVSGNYDFNHKVVIFV   750
 751  VINADGRESKIDINIADINPTSSRNTAMISIPLLGPTFKRTELTVSHDFS   800
 801  HPNRKSISAVAKFGRSESFINAKWNRSDGFDTLEGNIEAKSRFLGDFLIN   850
 851  VRYDMSNIADAHAEVDYLRTTTDGDKKEFKLNWTRKSTDDHLENEMVFDS   900
 901  NFETLSHARAYANADYGGIFKLLSGLDWDDKKISLTLEVRKNKISGILTT   950
 951  PFEGFETLEIDLQYKLTGKDKSVKATYQRGDRKASFNMEMSTKGKKGGSF  1000
1001  KVDLTTPFEVVKNLHIDGQYENKVAQINYQRNDIQMNFNGKANIKSSKAS  1050
1051  FDISFTPPSGQNIRIAASYDVQDFIDGTGDEEKELASLSLEFEGNSMDFS  1100
1101  LHGFRNDDRLYVMIHGTSSFAVLKMFHLKLDSELNTEARDGTFELTFNDF  1150
1151  KFNVSNHFERRANNGYYFRSKIESTLTPLPALIIGLGREGQERIITIGYG  1200
1201  EDKEITFSVKGKNNFLSGFSGKVDIPSIGYEGVEYDVDYSFPGDNHLQIK  1250
1251  VEIDLNENGQEVEATFFLDSEGIKARLSSAVLGDHSLRVRRSVAPDGFYA  1300
1301  EAGLDDYNLKLRGGFKNEDTARGVQLEGEVFGKRFLIDTLFQSEGKRYSE  1350
1351  GKLIIHTPFHGMEKMGGLFTWSNQNKKIMAHAELHLPSYTTPTITGEISL  1400
1401  DLKKKINGYVTLDVAGEEFTLKCNLAGSSISQGYTGSLEFYTTIPCCITC  1450
1451  CGDR                                                1454

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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