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

NucPred

Fetching Q8BY87 from www.uniprot.org...

The NucPred score for your sequence is 0.94 (see score help below)

   1  MVPGEENQLVPKEDVFWRCRQNIFDEMKKKFLQIENAAEEPRVLCIIQDT    50
  51  TNSKTVSERITLNLPASTPVRKLFEDVANKVGYINGTFDLTRENGVTTAD   100
 101  MAPLDHTSDKSLLDANFEPGKKNFLHLTDKDGEPPQMLLEDSNNVDDSVH   150
 151  DRFIGPLPREGSVASTNDYVSQNYSYSSILNKSETGYVGLVNQAMTCYLN   200
 201  SLLQTLFMTPEFRNALYKWEFEDSEEDPVTSIPYQLQRLFVLLQTSKKRA   250
 251  IETTDVTRSFGWDSSEAWQQHDVQELCRVMFDALEQKWKQTEQADLINEL   300
 301  YQGKLKDYVRCLECGYEGWRIDTYLDIPLVIRPYGSSQAFASVEEALHAF   350
 351  IQPEILDGPNQYFCERCKKKCDARKGLRFLHFPYLLTLQLKRFDFDYTTM   400
 401  HRIKLNDRMSFPEELDMSTFIDIEDEKSPQTESCTDSGAENEGSCHSDQM   450
 451  SNDFSTDDAVDEGICLESSSGSEKISKPGLEKNSLMYELFSVMVHSGSAA   500
 501  GGHYYACIKSFSDDQWYSFNDQHVSRITQEDIKKTHGGSSGSRGYYSSAF   550
 551  ASSTNAYMLIYRLKDPTRNAKFLEVDEYPEHIKNLVQKERELEEQEKRQR   600
 601  EIERNTCKIKLFCLHPVKQVMMENKLEVHKDKTLKEAVEMAYKMMDLEDV   650
 651  IPLDCCRLVKYDEFHDYLERSYEGEEDTPMGLLLGGVKSTYMFDLLLETR   700
 701  KPDQIFQSYKPGEVMVKVHVVDLKAETVAAPVTVRAYLNQTVTEFKQLIS   750
 751  KATHLPADSMRIVLERCYNDLRLLSMPSKTLKAEGFFRSNKVFVESSETV   800
 801  DHQAAFTDSHLWKLLDRHANTIRLFVLLPEQSPGSYSKRTAYQKAGGDSG   850
 851  NVDDDCERVKGPAGNLKSVDAILEESTEKLKSLSLQQQQQDGDNGDSSKS   900
 901  TETSDFENIESPLNERGSSTSVDNRELEQHIQTSDPENFQSEERSDSDVN   950
 951  NDRSTSSVDSDILSSSHSSDTLCNADSAQIPLANGLDSHSITSSRRTKAN  1000
1001  EGKKETWDTAEEDSGTDSEYDESGKSRGDMQYMYFKADPYTADEGSGEGH  1050
1051  KWLMVHVDKRITLAAFKQHLEPFVGVLSSHFKVFRVYTSNQEFETVRLNE  1100
1101  TLSSFSDDNKITIRLGRALKKGEYRVKVCQLLVNEQEPCKFLLDAVFAKG  1150
1151  MTVRQSKEELIPQLREQCGLDLSIDRFRLRKKTWKNPGTVFLDYHIYEED  1200
1201  INISSNWEVFLEVLDGVEKMKSMSQLAILTRRWRPAEMKLDPFQELVLES  1250
1251  NSVDELREKLSEISGIPLEDIEFAKGRGTFPCDISVLDIHQDLDWNPKVS  1300
1301  TLNVWPLYICDDGAVIFYRDRTEEVMELTDEQRNELMKKESSRLQKTGHR  1350
1351  VTYSPRKEKALKIYLDGAPNKDVAQD                          1376

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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