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

NucPred

Fetching Q9ULK4 from www.uniprot.org...

The NucPred score for your sequence is 0.43 (see score help below)

   1  METQLQSIFEEVVKTEVIEEAFPGMFMDTPEDEKTKLISCLGAFRQFWGG    50
  51  LSQESHEQCIQWIVKFIHGQHSPKRISFLYDCLAMAVETGLLPPRLVCES   100
 101  LINSDTLEWERTQLWALTFKLVRKIIGGVDYKGVRDLLKVILEKILTIPN   150
 151  TVSSAVVQQLLAAREVIAYILERNACLLPAYFAVTEIRKLYPEGKLPHWL   200
 201  LGNLVSDFVDTFRPTARINSICGRCSLLPVVNNSGAICNSWKLDPATLRF   250
 251  PLKGLLPYDKDLFEPQTALLRYVLEQPYSRDMVCNMLGLNKQHKQRCPVL   300
 301  EDQLVDLVVYAMERSETEEKFDDGGTSQLLWQHLSSQLIFFVLFQFASFP   350
 351  HMVLSLHQKLAGRGLIKGRDHLMWVLLQFISGSIQKNALADFLPVMKLFD   400
 401  LLYPEKEYIPVPDINKPQSTHAFAMTCIWIHLNRKAQNDNSKLQIPIPHS   450
 451  LRLHHEFLQQSLRNKSLQMNDYKIALLCNAYSTNSECFTLPMGALVETIY   500
 501  GNGIMRIPLPGTNCMASGSITPLPMNLLDSLTVHAKMSLIHSIATRVIKL   550
 551  AHAKSSVALAPALVETYSRLLVYMEIESLGIKGFISQLLPTVFKSHAWGI   600
 601  LHTLLEMFSYRMHHIQPHYRVQLLSHLHTLAAVAQTNQNQLHLCVESTAL   650
 651  RLITALGSSEVQPQFTRFLSDPKTVLSAESEELNRALILTLARATHVTDF   700
 701  FTGSDSIQGTWCKDILQTIMSFTPHNWASHTLSCFPGPLQAFFKQNNVPQ   750
 751  ESRFNLKKNVEEEYRKWKSMSNENDIITHFSMQGSPPLFLCLLWKMLLET   800
 801  DHINQIGYRVLERIGARALVAHVRTFADFLVYEFSTSAGGQQLNKCIEIL   850
 851  NDMVWKYNIVTLDRLILCLAMRSHEGNEAQVCYFIIQLLLLKPNDFRNRV   900
 901  SDFVKENSPEHWLQNDWHTKHMNYHKKYPEKLYFEGLAEQVDPPVQIQSP   950
 951  YLPIYFGNVCLRFLPVFDIVIHRFLELLPVSKSLETLLDHLGGLYKFHDR  1000
1001  PVTYLYNTLHYYEMHLRDRAFLKRKLVHAIIGSLKDNRPQGWCLSDTYLK  1050
1051  CAMNAREENPWVPDDTYYCRLIGRLVDTMAGKSPGPFPNCDWRFNEFPNP  1100
1101  AAHALHVTCVELMALAVSGKEVGNALLNVVLKSQPLVPRENITAWMNAIG  1150
1151  LIITALPEPYWIVLHDRIVSVISSPSLTSETEWVGYPFRLFDFTACHQSY  1200
1201  SEMSCSYTLALAHAVWHHSSIGQLSLIPKFLTEVLLPIVKTEFQLLYVYH  1250
1251  LVGPFLQRFQQERTRCMIEIGVAFYDMLLNVDQCSTHLNYMDPICDFLYH  1300
1301  MKYMFTGDSVKEQVEKIICNLKPALKLRLRFITHISKMEPAAVPPQAMNS  1350
1351  GSPAPQSNQVPVSLPVTQ                                  1368

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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