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

NucPred

Fetching Q8WWL7 from www.uniprot.org...

The NucPred score for your sequence is 0.87 (see score help below)

   1  MLLPLPPQSSKPVPKKSQSSKIVPSHHDPSEKTGENCQTKISPSSLQESP    50
  51  SSLQGALKKRSAFEDLTNASQCQPVQPKKEANKEFVKVVSKKINRNTHAL   100
 101  GLAKKNKRNLKWHKLEVTPVVASTTVVPNIMEKPLILDISTTSKTPNTEE   150
 151  ASLFRKPLVLKEEPTIEDETLINKSLSLKKCSNHEEVSLLEKLQPLQEES   200
 201  DSDDAFVIEPMTFKKTHKTEEAAITKKTLSLKKKMCASQRKQSCQEESLA   250
 251  VQDVNMEEDSFFMESMSFKKKPKTEESIPTHKLSSLKKKCTIYGKICHFR   300
 301  KPPVLQTTICGAMSSIKKPTTEKETLFQELSVLQEKHTTEHEMSILKKSL   350
 351  ALQKTNFKEDSLVKESLAFKKKPSTEEAIMMPVILKEQCMTEGKRSRLKP   400
 401  LVLQEITSGEKSLIMKPLSIKEKPSTEKESFSQEPSALQKKHTTQEEVSI   450
 451  LKEPSSLLKSPTEESPFDEALAFTKKCTIEEAPPTKKPLILKRKHATQGT   500
 501  MSHLKKPLILQTTSGEKSLIKEPLPFKEEKVSLKKKCTTQEMMSICPELL   550
 551  DFQDMIGEDKNSFFMEPMSFRKNPTTEETVLTKTSLSLQEKKITQGKMSH   600
 601  LKKPLVLQKITSEEESFYKKLLPFKMKSTTEEKFLSQEPSALKEKHTTLQ   650
 651  EVSLSKESLAIQEKATTEEEFSQELFSLHVKHTNKSGSLFQEALVLQEKT   700
 701  DAEEDSLKNLLALQEKSTMEEESLINKLLALKEELSAEAATNIQTQLSLK   750
 751  KKSTSHGKVFFLKKQLALNETINEEEFLNKQPLALEGYPSIAEGETLFKK   800
 801  LLAMQEEPSIEKEAVLKEPTIDTEAHFKEPLALQEEPSTEKEAVLKEPSV   850
 851  DTEAHFKETLALQEKPSIEQEALFKRHSALWEKPSTEKETIFKESLDLQE   900
 901  KPSIKKETLLKKPLALKMSTINEAVLFEDMIALNEKPTTGKELSFKEPLA   950
 951  LQESPTYKEDTFLKTLLVPQVGTSPNVSSTAPESITSKSSIATMTSVGKS  1000
1001  GTINEAFLFEDMITLNEKPTTGKELSFKEPLALQESPTCKEDTFLETFLI  1050
1051  PQIGTSPYVFSTTPESITEKSSIATMTSVGKSRTTTESSACESASDKPVS  1100
1101  PQAKGTPKEITPREDIDEDSSDPSFNPMYAKEIFSYMKEREEQFILTDYM  1150
1151  NRQIEITSDMRAILVDWLVEVQVSFEMTHETLYLAVKLVDLYLMKAVCKK  1200
1201  DKLQLLGATAFMIAAKFEEHNSPRVDDFVYICDDNYQRSEVLSMEINILN  1250
1251  VLKCDINIPIAYHFLRRYARCIHTNMKTLTLSRYICEMTLQEYHYVQEKA  1300
1301  SKLAAASLLLALYMKKLGYWVPFLEHYSGYSISELHPLVRQLNKLLTFSS  1350
1351  YDSLKAVYYKYSHPVFFEVAKIPALDMLKLEEILNCDCEAQGLVL       1395

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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