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

NucPred

Fetching O42131 from www.uniprot.org...

The NucPred score for your sequence is 0.71 (see score help below)

   1  MAKSGGGGGGGGGGGGGGGGSGGLTCVTLFDNQINASKKEESESVNKNDT    50
  51  SKKMSVERVYQKKTQLEHILLRPDTYIGSVEPLTQLMWVYDEDVGMNCRE   100
 101  VTFVPGLYKIFDEILVNAADNKQRDKNMTCIKISIDPESNIISIWNNGKG   150
 151  IPVVEHKVEKVYVPALIFGQLLTSSNYDDDEKKVTGGRNGYGAKLCNIFS   200
 201  TKFTVETACKEYKHSFKQTWMNNMMKTSEPKIKHFEGDDYTCITFQPDLS   250
 251  KFKMENLDKDIVSLMTRRAYDLAGSCKGVKVMLNGKKLPVNGFRSYVDLY   300
 301  VKDKLDETGVALKVIHEVVNERWDVCLTLSEKGFQQISFVNSIATTKGGR   350
 351  HVDYVVDQVVGKLIEVVKKKNKAGVSVKPFQVKNHIWVFVNCLIENPSFD   400
 401  SQTKENMTLQPKSFGSKCQLSEKFFKAASNCGIIESILNWVKFKAQTQLN   450
 451  KKCSSVKHSKIKGIPKLDDANDAGGKHSLDCTLILTEGDSAKSLAVSGLG   500
 501  VIGRDRYGVFPLRGKILNVREASHKQIMENAEINNIIKIVGLQYKKSYED   550
 551  PESLKSLRYGKIMIMTDQDQDGSHIKGLLINFIHHNWPSLLKHGFLEEFI   600
 601  TPIVKASKNKQELSFYSIPEFDEWKKHMENHKAWKIKYYKGLGTSTAKEA   650
 651  KEYFADMERHRILFRYAGPEDDAAITLAFSKKKIDDRKEWLTNFMEDRRQ   700
 701  RRLHGLPEQFLYGTATKHLTYNDFINKELILFSNSDNERSIPSLVDGLKP   750
 751  GQRKVLFTCFKRNDKREVKVAQLAGSVAEMSAYHHGEQALMMTIVNLAQN   800
 801  FVGSNNVNLLQPIGQFGTRLHGGKDAASPRYIFTMLSPLARLLFPSVDDN   850
 851  LLKFLYDDNQRVEPEWYIPIIPMVLVNGAEGIGTGWACKLPNYDTREIVN   900
 901  NVRRMLDGLDPHPMLPNYKNFRGTIQELGQNQYVVSGEIFVVDRNTVEIT   950
 951  ELPVRTWTQVYKEQVLEPMLNGTEKTPALISDYKEYHTDTTVKFVVKMTE  1000
1001  EKLAQAEAAGLHKVFKLQTSLTCNSMVLFDHMGCLKKYETVQDILKEFFD  1050
1051  LRLHYYSLRKEWLVGMLGAESTKLNNQARFILEKIQGKITIENRSKRDLI  1100
1101  QMLVQRGYESDPVKAWKEAQEKAAEEEDPQNANDDASSASGSTSGPDFNY  1150
1151  ILNMSLWSLTKEKVEELIKHRDSKERELNDLKRKSASDLWKEDLAAFVEE  1200
1201  LEKVEAQEREDVLAGMVGKPIKGKVGKPKMKKLQLEETMPSPFGRRIVPQ  1250
1251  ITSAMKADASRKLLKKKKGDADSVAIKMEFDEEFGGVQAEGGGDDTVNTA  1300
1301  ASGTKTPKLKREKKEPGTRVRRAPSSTKSSAKKVKKRNPWSDDESKSESD  1350
1351  LEESEPVIIPRDSLLRRAAADRAKYTFDFSKEEDDAHDDDDANNNNDLDE  1400
1401  LKVKASPVINDREDEFVPSDSVEKDEYDFSPVKSKPSPEKMSQEKKNQDF  1450
1451  GNIFSFPSYSQKTDDDTTKLDSDEEDSTPVFSSPFAPKQTEKMLSKTVAA  1500
1501  KKAKVDVPPKPKRAPKAKKMETVNSDSDSEFGIPKKTAAPKGKGRGAKKR  1550
1551  KTSGSENEGEYNPGKKAPKSTPCKKSKKAAFDQDSDVEIFQSGFASETAP  1600
1601  KPRTGRARKEVKYFAESDEDDDFDMFN                         1627

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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