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

NucPred

Fetching P40340 from www.uniprot.org...

The NucPred score for your sequence is 1.00 (see score help below)

   1  MARNLRNRRGSDVEDASNAKVGYETQIKDENGIIHTTTRSLRKINYAEIE    50
  51  KVFDFLEDDQVMDKDETPVDVTSDEHHNNNQKGDDEDDDVDLVSPHENAR   100
 101  TNEELTNERNLRKRKAHDPEEDDESFHEEDVDDDEEEEEADEFEDEYLDE   150
 151  DSKDNNRRRRAADRKFVVPDPDDDEEYDEDDEEGDRISHSASSKRLKRAN   200
 201  SRRTRSSRHPETPPPVRRALRSRTRHSRTSNEENDDENDNSRNEALTLAD   250
 251  EIRELQEDSPIREKRFLRERTKPVNYKLPPPLTASNAEEFIDKNNNALSF   300
 301  HNPSPARRGRGGWNASQNSGPTRRLFPTGGPFGGNDVTTIFGKNTNFYNQ   350
 351  VPSAFSDNNNNKLILDSDSSDDEILPLGVTPKTKKENTQKKKKKKPEIAD   400
 401  LDPLGVDMNVNFDDIGGLDNYIDQLKEMVALPLLYPELYQNFNITPPRGV   450
 451  LFHGPPGTGKTLMARALAASCSSDERKITFFMRKGADILSKWVGEAERQL   500
 501  RLLFEEAKKHQPSIIFFDEIDGLAPVRSSKQEQIHASIVSTLLALMDGMD   550
 551  NRGQVIVIGATNRPDAVDPALRRPGRFDREFYFPLPDVKARFKILQIQTR   600
 601  KWSSPLSTNFIDKLAFLTKGYGGADLRSLCTEAALISIQRSFPQIYRSND   650
 651  KLLVDPSKIKVKVSDFMLALKKIVPSSARSTGSSPQPLPELIKPLLADQL   700
 701  NNLKNKLDYMLNIKDTTFQRNTSLLQNFIDYEEYSGEEEEHDKYGGNEDT   750
 751  SSFRSYEFFESMAESQICKPRLLINGPKGNGQQYVGAAILNYLEEFNVQN   800
 801  LDLASLVSESSRTIEAAVVQSFMEAKKRQPSVVFIPNLDIWINTIPENVI   850
 851  LVLSGLFRSLQSNEKILLLCLAENLDISEVKNGILSDFAFDKNIFQLHKP   900
 901  SKENITRYFSNLIELLKTKPSDIPMKKRRVKPLPELQKVTSNAAPTNFDE   950
 951  NGEPLSEKVVLRRKLKSFQHQDMRLKNVLKIKLSGLMDLFKNRYKRFRKP  1000
1001  PIDDAFLVHLFEPETSNDPNWQPAYIKDENMILEVSTGRKFFNMDLDIVE  1050
1051  ERLWNGYYSEPKQFLKDIELIYRDANTIGDRERVIKASEMFANAQMGIEE  1100
1101  ISTPDFIQECKATRQRDLERQELFLEDEEKRAAMELEAKEQSQENILQEP  1150
1151  DLKDNKANEFGVAAGNQLQAQLQTTINTASIVNNSEVPQPIDTNLYKKEI  1200
1201  PAAIPSAVDKEKAVIPEDSGANEEYTTELIQATCTSEITTDDDERARKEP  1250
1251  KENEDSLQTQVTEENFSKIDANTNNINHVKEIQSVNKPNSLHETVEKRER  1300
1301  SPIPKEVVEPEQGKKSDKELILTPEQIKKVSACLIEHCQNFTVSQLEDVH  1350
1351  SSVAKIIWKSKSAWDKTGTVDEIIKFLSE                       1379

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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