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

NucPred

Fetching Q9S775 from www.uniprot.org...

The NucPred score for your sequence is 0.89 (see score help below)

   1  MSSLVERLRIRSDRKPVYNLDDSDDDDFVPKKDRTFEQVEAIVRTDAKEN    50
  51  ACQACGESTNLVSCNTCTYAFHAKCLVPPLKDASVENWRCPECVSPLNEI   100
 101  DKILDCEMRPTKSSEQGSSDAEPKPIFVKQYLVKWKGLSYLHCSWVPEKE   150
 151  FQKAYKSNHRLKTRVNNFHRQMESFNNSEDDFVAIRPEWTTVDRILACRE   200
 201  EDGELEYLVKYKELSYDECYWESESDISTFQNEIQRFKDVNSRTRRSKDV   250
 251  DHKRNPRDFQQFDHTPEFLKGLLHPYQLEGLNFLRFSWSKQTHVILADEM   300
 301  GLGKTIQSIALLASLFEENLIPHLVIAPLSTLRNWEREFATWAPQMNVVM   350
 351  YFGTAQARAVIREHEFYLSKDQKKIKKKKSGQISSESKQKRIKFDVLLTS   400
 401  YEMINLDSAVLKPIKWECMIVDEGHRLKNKDSKLFSSLTQYSSNHRILLT   450
 451  GTPLQNNLDELFMLMHFLDAGKFGSLEEFQEEFKDINQEEQISRLHKMLA   500
 501  PHLLRRVKKDVMKDMPPKKELILRVDLSSLQKEYYKAIFTRNYQVLTKKG   550
 551  GAQISLNNIMMELRKVCCHPYMLEGVEPVIHDANEAFKQLLESCGKLQLL   600
 601  DKMMVKLKEQGHRVLIYTQFQHMLDLLEDYCTHKKWQYERIDGKVGGAER   650
 651  QIRIDRFNAKNSNKFCFLLSTRAGGLGINLATADTVIIYDSDWNPHADLQ   700
 701  AMARAHRLGQTNKVMIYRLINRGTIEERMMQLTKKKMVLEHLVVGKLKTQ   750
 751  NINQEELDDIIRYGSKELFASEDDEAGKSGKIHYDDAAIDKLLDRDLVEA   800
 801  EEVSVDDEEENGFLKAFKVANFEYIDENEAAALEAQRVAAESKSSAGNSD   850
 851  RASYWEELLKDKFELHQAEELNALGKRKRSRKQLVSIEEDDLAGLEDVSS   900
 901  DGDESYEAESTDGEAAGQGVQTGRRPYRRKGRDNLEPTPLMEGEGRSFRV   950
 951  LGFNQSQRAIFVQTLMRYGAGNFDWKEFVPRLKQKTFEEINEYGILFLKH  1000
1001  IAEEIDENSPTFSDGVPKEGLRIEDVLVRIALLILVQEKVKFVEDHPGKP  1050
1051  VFPSRILERFPGLRSGKIWKEEHDKIMIRAVLKHGYGRWQAIVDDKELGI  1100
1101  QELICKELNFPHISLSAAEQAGLQGQNGSGGSNPGAQTNQNPGSVITGNN  1150
1151  NASADGAQVNSMFYYRDMQRRLVEFVKKRVLLLEKAMNYEYAEEYYGLGG  1200
1201  SSSIPTEEPEAEPKIADTVGVSFIEVDDEMLDGLPKTDPITSEEIMGAAV  1250
1251  DNNQARVEIAQHYNQMCKLLDENARESVQAYVNNQPPSTKVNESFRALKS  1300
1301  INGNINTILSITSDQSKSHEDDTKPDLNNVEMKDTAEETKPLRGGVVDLN  1350
1351  VVEGEENIAEASGSVDVKMEEAKEEEKPKNMVVD                  1384

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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