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

NucPred

Fetching O14139 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MSTSAIALALSSSKAIEQLDHVQTETPNLKQEMSESPSNSGVASKRKLQS    50
  51  TEWLDPELYGLRRSGRTRSNPGRYVDTDDQEDVFPSKHRKGTRNGSSFSR   100
 101  HRTIRDLDDEAESVTSEESESDDSSYGGTPKKRSRQKKSNTYVQDEIRFS   150
 151  SRNSKGVNYNEDAYFESFEEEEEEEMYEYATEVSEEPEDTRAIDVVLDHR   200
 201  LIEGHDGSTPSEDYEFLIKWVNFSHLHCTWEPYNNISMIRGSKKVDNHIK   250
 251  QVILLDREIREDPTTTREDIEAMDIEKERKRENYEEYKQVDRIVAKHLNS   300
 301  DGSVEYLVKWKQLLYDFCTWEASSIIEPIAATEIQAFQEREESALSPSRG   350
 351  TNYGNSRPKYRKLEQQPSYITGGELRDFQLTGVNWMAYLWHKNENGILAD   400
 401  EMGLGKTVQTVAFLSYLAHSLRQHGPFLVVVPLSTVPAWQETLALWASDM   450
 451  NCISYLGNTTSRQVIRDYEFYVDGTQKIKFNLLLTTYEYVLKDRSVLSNI   500
 501  KWQYMAIDEAHRLKNSESSLYEALSQFKNSNRLLITGTPLQNNIRELAAL   550
 551  VDFLMPGKFEIREEINLEAPDEEQEAYIRSLQEHLQPYILRRLKKDVEKS   600
 601  LPSKSERILRVELSDLQMYWYKNILTRNYRVLTQSISSGSQISLLNIVVE   650
 651  LKKASNHPYLFDGVEESWMQKINSQGRRDEVLKGLIMNSGKMVLLDKLLS   700
 701  RLRRDGHRVLIFSQMVRMLDILGDYLSLRGYPHQRLDGTVPAAVRRTSID   750
 751  HFNAPNSPDFVFLLSTRAGGLGINLMTADTVIIFDSDWNPQADLQAMARA   800
 801  HRIGQKNHVMVYRLLSKDTIEEDVLERARRKMILEYAIISLGVTDKQKNS   850
 851  KNDKFSAEELSAILKFGASNMFKAENNQKKLEDMNLDEILEHAEDHDTSN   900
 901  DVGGASMGGEEFLKQFEVTDYKADVSWDDIIPLTEREKFEEEDRLREEEE   950
 951  ALKQEIELSSRRGNRPYPSSAVESPSYSGTSERKSKKQMLKDEVLLEKEI  1000
1001  RLLYRAMIRYGSLEHRYNDIVKYADLTTQDAHVIKKIAADLVTASRKAVS  1050
1051  AAEKDLSNDQSNNKSSRKALLITFKGVKNINAETLVQRLNDLDILYDAMP  1100
1101  TSGYSNFQIPMHVRSVHGWSCQWGPREDSMLLSGICKHGFGAWLEIRDDP  1150
1151  ELKMKDKIFLEDTKQTDNSVPKDKENKEKKVPSAVHLVRRGEYLLSALRE  1200
1201  HHQNFGIKSSPAISTNGKTQPKKQTANRRQSGKPNVKSAQKIESATRTPS  1250
1251  PAISESRKKPSSKDTKIETPSREQSRSQTASPVKSEKDDGNVSLNAEQKA  1300
1301  RCKELMYPVRKHMKRLRKDSSGLGRAELVKLLTECLTTIGKHIEKTVNDT  1350
1351  PSEEKATVRKNLWMFACYFWPKEEVKYTSLISMYEKMK              1388

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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