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

NucPred

Fetching Q8GZ52 from www.uniprot.org...

The NucPred score for your sequence is 0.51 (see score help below)

   1  MIQTGEEDEEKATSLEVEFASGNGVDDEEELRLQWATVERLPTFKRVTTA    50
  51  LLARDEVSGKGRVIDVTRLEGAERRLLIEMLVKQIEDDNLRLLRKIRKRI   100
 101  DKVGIELPTVEVRFNNLSVEAECQVIHGKPIPTLWNTIKGLLSEFICSKK   150
 151  ETKIGILKGVSGIVRPGRMTLLLGPPGCGKTTLLQALSGKFSDSVKVGGE   200
 201  VCYNGCSLSEFIPEKTSSYISQNDLHIPELSVRETLDFSACCQGIGSRME   250
 251  IMKEISRMEKLQEIIPDPAVDAYMKATSVEGLKNNLQTDYILKILGLDIC   300
 301  ADTRVGDATRPGISGGEKRRLTTGELVVGPATTLFMDEISNGLDSSTTFQ   350
 351  IVSCLQQLAHIAEATILISLLQPAPETFELFDDVILMGEGKIIYHAPRAD   400
 401  ICRFFEEFGFKCPERKGVADFLQEIMSKKDQEQYWCHRDKPYSYISVDSF   450
 451  INKFKESNLGLLLKEELSKPFNKSQTRKDGLCYKKYSLGKWEMLKACSRR   500
 501  EFLLMKRNSFIYLFKSALLVFNALVTMTVFLQVGATTDSLHGNYLMGSLF   550
 551  TALFRLLADGLPELTLTISRLGVFCKQKDLYFYPAWAYAIPSIILKIPLS   600
 601  VLDSFIWTLLTYYVIGYSPEVKRFFLQFLILSTFNLSCVSMFRAIAAIFR   650
 651  TIIASTITGAISILVLSLFGGFVIPKSSMPAWLGWGFWLSPLSYAEIGLT   700
 701  ANEFFSPRWSKVISSKTTAGEQMLDIRGLNFGRHSYWTAFGALVGFVLFF   750
 751  NALYVLALTYQNNPQRSRAIISHEKYSRPIEEDFKPCPKITSRAKTGKII   800
 801  LPFKPLTVTFQNVQYYIETPQGKTRQLLSDITGALKPGVLTSLMGVSGAG   850
 851  KTTLLDVLSGRKTRGIIKGEIKVGGYPKVQETFARVSGYCEQFDIHSPNI   900
 901  TVEESLKYSAWLRLPYNIDSKTKNELVKEVLETVELDDIKDSVVGLPGIS   950
 951  GLSIEQRKRLTIAVELVANPSIIFMDEPTTGLDARAAAIVMRAVKNVAET  1000
1001  GRTVVCTIHQPSIDIFETFDELILMKNGGQLVYYGPPGQNSSKVIEYFES  1050
1051  FSGLPKIQKNCNPATWILDITSKSAEEKLGIDFSQSYKDSTLYKQNKMVV  1100
1101  EQLSSASLGSEALRFPSQFSQTAWVQLKACLWKQHYSYWRNPSHNITRIV  1150
1151  FILLDSTLCGLLFWQKAEDINNQQDLISIFGSMYTLVVFPGMNNCAAVIN  1200
1201  FIAAERNVFYRERFARMYSSWAYSFSQVLIEVPYSLLQSLLCTIIVYPTI  1250
1251  GYHMSVYKMFWSLYSIFCSLLIFNYSGMLMVALTPNIHMAVTLRSSFFSM  1300
1301  LNLFAGFVIPKQKIPKWWIWMYYLSPTSWVLEGLLSSQYGDVDKEILVFG  1350
1351  EKKRVSAFLEDYFGYKHESLAVVAFVLIAYPIIVATLFAFFMSKLSFQKK  1400

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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