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

NucPred

Fetching Q2QLG5 from www.uniprot.org...

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

   1  MKAPAVLAPGILVLLFTLVQRSNGECKEALTKSEMNVNMKYQLPNFTAET    50
  51  PIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYRTGPVLEHPDCFPCQD   100
 101  CSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNH   150
 151  TADIQSEVHCIFSAQTEEPSQCPDCMVSALGTKVLLSVKDRFLNFFVGNT   200
 201  INSSYFPDHSLHSISVRRLKETKDGFMFLTDQSYVDVLPEFRDSYPIKYV   250
 251  HAFESNNFIYFLTVQRETLNAQTFHTRIIRFCSINSALHSYMEMPLECIL   300
 301  TEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSK   350
 351  PDSAEPMDRSAVCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNR   400
 401  TFQRNLLGCEARHDEYRTEFTTALQRIDLFAGQFNKVLLTSISTFIKGDL   450
 451  TIANLGTSEGRFIQIVVSRSVPSNPHVNFLLDSHPVSPEVIVEHPLNQNG   500
 501  YTLVVTGKKITKIPLNGLGCRHFQSCSQCLSAPSFVQCGWCHDKCVRSEE   550
 551  CSSGTWTQETCLPTIYKVFPTSAPLEGGTRLTICGWDFGFRRNNKFDLKK   600
 601  TRVLLGNESCTLTLSESTMNTLKCTVGPAMNERFNMSIIISNAHGTTQYS   650
 651  TFSYVDPIITSISPRYGPMSGGTLLTLTGNYLNSGNSRHISIGGKTCTLK   700
 701  SVSNSILECYTPAQSISTEFPVKLKIDLANRETSIFSYREDPIVYEIYPT   750
 751  KSFVSGGSTITGIGKNLNSVSVPRMVINLHEAKRNFTVACQHRSNSEIIC   800
 801  CTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMI   850
 851  SIGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPGDL   900
 901  LKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLVAGVVSISIALLLLLG   950
 951  LFLWLKKKKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNES  1000
1001  VDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNT  1050
1051  VHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDN  1100
1101  DGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSE  1150
1151  GSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKF  1200
1201  VHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWM  1250
1251  ALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGR  1300
1301  RLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYV  1350
1351  HVNATYVNVKCVAPYPSLLSSQDNTDGEVDT                     1381

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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