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

NucPred

Fetching Q2QLA9 from www.uniprot.org...

The NucPred score for your sequence is 0.48 (see score help below)

   1  MKAPAVLAPGILVLLFTLVQKSDGECKEALVKSEMNVNMKYQLPNFTAET    50
  51  PIQNVVLHKHHIYLGATNYIYVLNDKDLQKVAEYKTGPVLEHPDCFPCQD   100
 101  CSRKANLSGGAWKDNINMALLVDTYYDDQLISCGSVHRGTCQRHVLPLNN   150
 151  VADIQSEVYCMYSPQAEEPHQCPDCVVSALGTKVLLSEKDRFVTFFVGNT   200
 201  INSSYLPDHSLHSISVRRLKETQDGFKFLTDQSYIDVLPEFRDSYPIKYI   250
 251  HAFESNHFIYFLTVQRETLDAQTFHTRIIRFCSVDSGLHSYMEMPLECIL   300
 301  TEKRRKRSTSEEVFNILQAAYVSKPGAHLAKQIGANLNDDILYGVFAQSK   350
 351  PDSAEPMNRSAVCAFPVKYVNEFFNKIVNKNNVRCLQHFYGPHHEHCFNR   400
 401  TLLRNSSGCEVRNDEYRTEFTTALQRVDLFMGQFNQVLLTSISTFIKGNL   450
 451  TIANLGTSEGRFMQVVVSRSGSSTPHVNFHLDSHPVSPEVIVEHPLNQNG   500
 501  YTLVVTGKKITKIPLNGLGCEHFQSCSQCLSAPPFVQCGWCHDKCVRLEE   550
 551  CHNGTWTQEICLPTIYKVFPTSAPLEGGTTLTVCGWDFGFRKNNKLDSKK   600
 601  TKVLLGNESCTLTLSESTSNTLKCTVGPAMNERFNISITVSNSRGTARYS   650
 651  TFSYVDPIITSISPSYGPKTGGTLLTLTGKYLNSGNSRHISIGGKTCTLK   700
 701  SVSDSILECYTPAQTTPTEFPVKLKIDLANREMNSFSYREDPIVYEIHPT   750
 751  KSFISGGSTITGVGKNLNSVSVLRMVINVREAGRNFTVACQHRSNSEIIC   800
 801  CTTPSLQQLNLQLPLKTKAFFMLDGIHSKYFDLIYVHNPVFKPFEKPVMI   850
 851  SIGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHSHSEAVLCTVPSDL   900
 901  LKLNSELNIEWKQAVSSTILGKVIVQPDQNFTGLIVGVVSISIILLLLLG   950
 951  LFLWLKRRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNES  1000
1001  VDYRATFPEDQFPNSSQNGSCRQVQYPLTDLSPILTSGDSDISSPLLQNT  1050
1051  VHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDN  1100
1101  DDKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSE  1150
1151  GSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKF  1200
1201  VHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWM  1250
1251  ALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGR  1300
1301  RLLQPEYCPDPLYEVMLKCWHPKAELRPSFSELVSRISAIFSTFIGEHYV  1350
1351  HVNATYVNVKCVAPYPSLLSSQDNVDGEVDT                     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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