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

NucPred

Fetching Q14678 from www.uniprot.org...

The NucPred score for your sequence is 0.83 (see score help below)

   1  MAHTTKVNGSASGKAGDILSGDQDKEQKDPYFVETPYGYQLDLDFLKYVD    50
  51  DIQKGNTIKRLNIQKRRKPSVPCPEPRTTSGQQGIWTSTESLSSSNSDDN   100
 101  KQCPNFLIARSQVTSTPISKPPPPLETSLPFLTIPENRQLPPPSPQLPKH   150
 151  NLHVTKTLMETRRRLEQERATMQMTPGEFRRPRLASFGGMGTTSSLPSFV   200
 201  GSGNHNPAKHQLQNGYQGNGDYGSYAPAAPTTSSMGSSIRHSPLSSGIST   250
 251  PVTNVSPMHLQHIREQMAIALKRLKELEEQVRTIPVLQVKISVLQEEKRQ   300
 301  LVSQLKNQRAASQINVCGVRKRSYSAGNASQLEQLSRARRSGGELYIDYE   350
 351  EEEMETVEQSTQRIKEFRQLTADMQALEQKIQDSSCEASSELRENGECRS   400
 401  VAVGAEENMNDIVVYHRGSRSCKDAAVGTLVEMRNCGVSVTEAMLGVMTE   450
 451  ADKEIELQQQTIESLKEKIYRLEVQLRETTHDREMTKLKQELQAAGSRKK   500
 501  VDKATMAQPLVFSKVVEAVVQTRDQMVGSHMDLVDTCVGTSVETNSVGIS   550
 551  CQPECKNKVVGPELPMNWWIVKERVEMHDRCAGRSVEMCDKSVSVEVSVC   600
 601  ETGSNTEESVNDLTLLKTNLNLKEVRSIGCGDCSVDVTVCSPKECASRGV   650
 651  NTEAVSQVEAAVMAVPRTADQDTSTDLEQVHQFTNTETATLIESCTNTCL   700
 701  STLDKQTSTQTVETRTVAVGEGRVKDINSSTKTRSIGVGTLLSGHSGFDR   750
 751  PSAVKTKESGVGQININDNYLVGLKMRTIACGPPQLTVGLTASRRSVGVG   800
 801  DDPVGESLENPQPQAPLGMMTGLDHYIERIQKLLAEQQTLLAENYSELAE   850
 851  AFGEPHSQMGSLNSQLISTLSSINSVMKSASTEELRNPDFQKTSLGKITG   900
 901  NYLGYTCKCGGLQSGSPLSSQTSQPEQEVGTSEGKPISSLDAFPTQEGTL   950
 951  SPVNLTDDQIAAGLYACTNNESTLKSIMKKKDGNKDSNGAKKNLQFVGIN  1000
1001  GGYETTSSDDSSSDESSSSESDDECDVIEYPLEEEEEEEDEDTRGMAEGH  1050
1051  HAVNIEGLKSARVEDEMQVQECEPEKVEIRERYELSEKMLSACNLLKNTI  1100
1101  NDPKALTSKDMRFCLNTLQHEWFRVSSQKSAIPAMVGDYIAAFEAISPDV  1150
1151  LRYVINLADGNGNTALHYSVSHSNFEIVKLLLDADVCNVDHQNKAGYTPI  1200
1201  MLAALAAVEAEKDMRIVEELFGCGDVNAKASQAGQTALMLAVSHGRIDMV  1250
1251  KGLLACGADVNIQDDEGSTALMCASEHGHVEIVKLLLAQPGCNGHLEDND  1300
1301  GSTALSIALEAGHKDIAVLLYAHVNFAKAQSPGTPRLGRKTSPGPTHRGS  1350
1351  FD                                                  1352

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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