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

NucPred

Fetching P97523 from www.uniprot.org...

The NucPred score for your sequence is 0.43 (see score help below)

   1  MKAPTALAPGILLLLLTLAQRSHGECKEALVKSEMNVNMKYQLPNFTAET    50
  51  PIHNVVLPGHHIYLGATNYIYVLNDKDLQKVSEFKTGPVVEHPDCFPCQD   100
 101  CSSKANVSGGVWKDNVNMALLVDTYYDDQLISCGSVNRGTCQRHVLPPDN   150
 151  AADIQSEVHCMFSPLAEEESGQCPDCVVSALGAKVLLSEKDRFINFFVGN   200
 201  TINSSYPPDYSLHSISVRRLKETQDGFKFLTDQSYIDVLPEFRDSYPIKY   250
 251  IHAFESNHFIYFLTVQKETLDAQTFHTRIIRFCSVDSGLHSYMEMPLECI   300
 301  LTEKRRKRSTREEVFNILQAAYVSKPGANLAKQIGASPYDDILYGVFAQS   350
 351  KPDSAEPMNRSAVCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFN   400
 401  RTLLRNSSGCEVRSDEYRTEFTTALQRVDLFMGRLNHVLLTSISTFIKGD   450
 451  LTIANLGTSEGRFMQVVLSRTAHFTPHVNFLLDSYPVSPEVIVEHPSNQN   500
 501  GYTLVVTGKKITKIPLNGLGCGHFQSCSQCLSPPYFIQCGWCHNRCVHSN   550
 551  ECPSGTWTQEICLPAVYKVFPTSAPLEGGTMLTICGWDFGFKKNNKFDLR   600
 601  KTKVLLGNESCTLTLSESTTNTLKCTVGPAMSEHFNVSVIVSNSRETTQY   650
 651  SAFSYVDPVITSISPRYGPHAGGTLLTLTGKYLNSGNSRHISIGGKTCTL   700
 701  KSVSDSILECYTPGHTVSAEFPVKLKIDLADRVTSSFSYREDPVVSEIHP   750
 751  TKSFISGGSTITGIGKNLNSVSTPKLVIEVHDVGVNYTVACQHRSSSEII   800
 801  CCTTPSLRQLDLQLPLKTKAFFLLDGILSKHFDLTYVHDPMFKPFEKPVM   850
 851  ISMGNENVVEIKGDDIDPEAVKGEVLKVGNKSCENLHWHSEALLCTVPSD   900
 901  LLKLNGGELNIEWKQAVSSTVLGKVIVQPDQNFAGLIIGAVSISVVVLLV   950
 951  SGLFLWLRKRKHKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNE  1000
1001  SVDYRATFPEDQFPNSSQNGACRQVQYLLTDLSPILTSGDSDISSPLLQN  1050
1051  TVHIDLSALNPELVQAVPHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLD  1100
1101  SDGKKIHCAVKSLNRITDIEEVSQFLTEGIIMKDFSHPNVLSLLGICLRS  1150
1151  EGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLVSKK  1200
1201  FVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKW  1250
1251  MALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITIYLLQG  1300
1301  RRLLQPEYCPDALYEVMLKCWHPKAEMRPSVSELVSRISSIFSTFIGEHY  1350
1351  VHVNATYVNVKCVAPYPSLLPSQDNIDGEANT                    1382

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.)

Go back to the NucPred Home Page.