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

NucPred

Fetching Q63425 from www.uniprot.org...

The NucPred score for your sequence is 0.31 (see score help below)

   1  MEARSRSAEELRRAELVEIIVETEAQTGVSGFNVAGGGKEGIFVRELRED    50
  51  SPAAKSLSLQEGDQLLSARVFFENFKYEDALRLLQCAEPYKVSFCLKRTV   100
 101  PTGDLALRPGTVSGYEMKGPRAKVAKLNIQSLSPVKKKKMVIGTLGTPAD   150
 151  LAPVDVEFSFPKFSRLRRGLKADAVKGPVPAAPARRRLQLPRLRVREVAE   200
 201  EAQVARMAAAAPPSRKAKSEAEVATGAGFTAPQIELVGPRLPSAEVGVPK   250
 251  VSVPKGTPSTEAASGFALHLPTLGLGAPAAPAVEPPTTGIQVPQVELPTL   300
 301  PSLPTLPTLPCLDTQEGAAVVKVPTLDVAAPSVEVDLALPGAEVEAQGEV   350
 351  PEVALKMPRLSFPRFGVRGKEATEAKVVKGSPEAKAKGPRLRMPTFGLSL   400
 401  LESRPSGPEVAAESKLKLPTLKMPSFGISVAGPEVKAPKGPEVKLPKVPE   450
 451  IKLPKAPEAAIPDVQLPEVQLPKMSDMKLPKIPEMAVPDVHLPEVKLPKV   500
 501  PEMKVPEMKLPKIPEMAVPDVHLPDIQLPKVPEMKLPDMKLPKVPEMAVP   550
 551  DVHLPDIQLPKVPEMKLPDMKLPKVPEMAVPDVRIPEVQLPKVSEVKLPK   600
 601  IPDMAVPDVRLPELQLPKMSEVKLPKIPDMAVPDVRLPEVQLPKVSELKL   650
 651  PKVPEMTMPDIRLPEVQLPKVPDIKLPEIKLPKVPEMAVPDVPLPELQLP   700
 701  KVPQVPDVHLPKVPEMKLPKVPEAQRKSAGAEQAEKTEFSFKLPKMTVPK   750
 751  LGKVTKPGEAGIEVPDKLLILPCLQPEVGTEVARVGVPSLSLPSVELDLP   800
 801  GALGLEGQVQEAVSGKVEKPEGPRVAVGTGEAGFRVPSVEIVNPQLPTVE   850
 851  VKKEQLEMVEMKVKPTSKFSLPKFGLSGPKAVKAEVEGPGRATKLKVSKF   900
 901  AISLPRARAGTDADAKGAGEAGLLPALDLSIPQLSLDAQLPSGKVEVAGA   950
 951  ESKPKGSRFALPKFGAKGRDSEADVLVAGEAELEGKGWGWDGKVKMPKLK  1000
1001  MPSFGLSRGKEAEIQDGRVSPGEKLEAIAGQLKIPEVELVTPGAQETEKV  1050
1051  TSGVKPSGLQVSTTRQVVAEGQEGAQRVSSLGISLPQVELASFGEAGPEI  1100
1101  AAPSAEGTVGSRIQVPQVMLELPGTQVAGGDLLVGEGIFKMPTVTVPQLE  1150
1151  LDVGLGHEAQAGETAKSEGGLKLKLPTLGAGGKGEGAEAQSPEAQHTFHI  1200
1201  SLPDVELTSPVSSHAEYQVVEGDGDGGHKLKVRLPLFGLARAKEGIETGE  1250
1251  KVKSPKLRLPRVGFSQSESASGEGSPSPEEEEEGSGEGASGRRGRVRVRL  1300
1301  PRVGLASPSKGSKGQEGDAASKSPVGEKSPKFRFPRVSLSPKARSGSKDR  1350
1351  EEGGFRVRLPSVGFSETAAPGSARIEGTQAAAI                   1383

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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