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

NucPred

Fetching P16056 from www.uniprot.org...

The NucPred score for your sequence is 0.50 (see score help below)

   1  MKAPTVLAPGILVLLLSLVQRSHGECKEALVKSEMNVNMKYQLPNFTAET    50
  51  PIQNVVLHGHHIYLGATNYIYVLNDKDLQKVSEFKTGPVLEHPDCLPCRD   100
 101  CSSKANSSGGVWKDNINMALLVDTYYDDQLISCGSVNRGTCQRHVLPPDN   150
 151  SADIQSEVHCMFSPEEESGQCPDCVVSALGAKVLLSEKDRFINFFVGNTI   200
 201  NSSYPPGYSLHSISVRRLKETQDGFKFLTDQSYIDVLPEFLDSYPIKYIH   250
 251  AFESNHFIYFLTVQKETLDAQTFHTRIIRFCSVDSGLHSYMEMPLECILT   300
 301  EKRRKRSTREEVFNILQAAYVSKPGANLAKQIGASPSDDILFGVFAQSKP   350
 351  DSAEPVNRSAVCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRT   400
 401  LLRNSSGCEARSDEYRTEFTTALQRVDLFMGRLNQVLLTSISTFIKGDLT   450
 451  IANLGTSEGRFMQVVLSRTAHLTPHVNFLLDSHPVSPEVIVEHPSNQNGY   500
 501  TLVVTGKKITKIPLNGLGCGHFQSCSQCLSAPYFIQCGWCHNQCVRFDEC   550
 551  PSGTWTQEICLPAVYKVFPTSAPLEGGTVLTICGWDFGFRKNNKFDLRKT   600
 601  KVLLGNESCTLTLSESTTNTLKCTVGPAMSEHFNVSVIISNSRETTQYSA   650
 651  FSYVDPVITSISPRYGPQAGGTLLTLTGKYLNSGNSRHISIGGKTCTLKS   700
 701  VSDSILECYTPAQTTSDEFPVKLKIDLANRETSSFSYREDPVVYEIHPTK   750
 751  SFISGGSTITGIGKTLNSVSLPKLVIDVHEVGVNYTVACQHRSNSEIICC   800
 801  TTPSLKQLGLQLPLKTKAFFLLDGILSKHFDLTYVHNPVFEPFEKPVMIS   850
 851  MGNENVVEIKGNNIDPEAVKGEVLKVGNQSCESLHWHSGAVLCTVPSDLL   900
 901  KLNSELNIEWKQAVSSTVLGKVIVQPDQNFAGLIIGAVSISVVVLLLSGL   950
 951  FLWMRKRKHKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVD  1000
1001  YRATFPEDQFPNSSQNGACRQVQYPLTDLSPILTSGDSDISSPLLQNTVH  1050
1051  IDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDG  1100
1101  KKIHCAVKSLNRITDIEEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGS  1150
1151  PLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVH  1200
1201  RDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMAL  1250
1251  ESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITIYLLQGRRL  1300
1301  LQPEYCPDALYEVMLKCWHPKAEMRPSFSELVSRISSIFSTFIGEHYVHV  1350
1351  NATYVNVKCVAPYPSLLPSQDNIDGEGNT                       1379

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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