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

NucPred

Fetching O64827 from www.uniprot.org...

The NucPred score for your sequence is 0.81 (see score help below)

   1  MEVKMDELVLDVDVEEATGSELLVKSEPEADLNAVKSSTDLVTVTGPIGK    50
  51  NGEGESSPSEPKWLQQDEPIALWVKWRGKWQAGIRCAKADWPLTTLRGKP   100
 101  THDRKKYCVIFFPHTKNYSWADMQLVRSINEFPDPIAYKSHKIGLKLVKD   150
 151  LTAARRYIMRKLTVGMFNIVDQFPSEVVSEAARDIIIWKEFAMEATRSTS   200
 201  YHDLGIMLVKLHSMILQRYMDPIWLENSFPLWVQKCNNAVNAESIELLNE   250
 251  EFDNCIKWNEVKSLSESPMQPMLLSEWKTWKHDIAKWFSISRRGVGEIAQ   300
 301  PDSKSVFNSDVQASRKRPKLEIRRAETTNATHMESDTSPQGLSAIDSEFF   350
 351  SSRGNTNSPETMKEENPVMNTPENGLDLWDGIVVEAGGSQFMKTKETNGL   400
 401  SHPQDQHINESVLKKPFGSGNKSQQCIAFIESKGRQCVRWANEGDVYCCV   450
 451  HLASRFTTKSMKNEGSPAVEAPMCGGVTVLGTKCKHRSLPGFLYCKKHRP   500
 501  HTGMVKPDDSSSFLVKRKVSEIMSTLETNQCQDLVPFGEPEGPSFEKQEP   550
 551  HGATSFTEMFEHCSQEDNLCIGSCSENSYISCSEFSTKHSLYCEQHLPNW   600
 601  LKRARNGKSRIISKEVFVDLLRGCLSREEKLALHQACDIFYKLFKSVLSL   650
 651  RNSVPMEVQIDWAKTEASRNADAGVGEFLMKLVSNERERLTRIWGFATGA   700
 701  DEEDVSLSEYPNRLLAITNTCDDDDDKEKWSFSGFACAICLDSFVRRKLL   750
 751  EIHVEERHHVQFAEKCMLLQCIPCGSHFGDKEQLLVHVQAVHPSECKSLT   800
 801  VASECNLTNGEFSQKPEAGSSQIVVSQNNENTSGVHKFVCKFCGLKFNLL   850
 851  PDLGRHHQAEHMGPSLVGSRGPKKGIRFNTYRMKSGRLSRPNKFKKSLGA   900
 901  VSYRIRNRAGVNMKRRMQGSKSLGTEGNTEAGVSPPLDDSRNFDGVTDAH   950
 951  CSVVSDILLSKVQKAKHRPNNLDILSAARSACCRVSVETSLEAKFGDLPD  1000
1001  RIYLKAAKLCGEQGVQVQWHQEGYICSNGCKPVKDPNLLHPLIPRQENDR  1050
1051  FGIAVDAGQHSNIELEVDECHCIMEAHHFSKRPFGNTAVLCKDISFGKES  1100
1101  VPICVVDDDLWNSEKPYEMPWECFTYVTNSILHPSMDLVKENLQLRCSCR  1150
1151  SSVCSPVTCDHVYLFGNDFEDARDIYGKSMRCRFPYDGKQRIILEEGYPV  1200
1201  YECNKFCGCSRTCQNRVLQNGIRAKLEVFRTESKGWGLRACEHILRGTFV  1250
1251  CEYIGEVLDQQEANKRRNQYGNGDCSYILDIDANINDIGRLMEEELDYAI  1300
1301  DATTHGNISRFINHSCSPNLVNHQVIVESMESPLAHIGLYASMDIAAGEE  1350
1351  ITRDYGRRPVPSEQENEHPCHCKATNCRGLLS                    1382

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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