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

NucPred

Fetching O77819 from www.uniprot.org...

The NucPred score for your sequence is 0.84 (see score help below)

   1  MSTGDSFETRFEKIDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNK    50
  51  NIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRK   100
 101  VYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLY   150
 151  MVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVK   200
 201  PDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQG   250
 251  GDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFP   300
 301  DDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRD   350
 351  TVAPVVPDLSSDIDTSNFDDLEEDKGDEETFPIPKAFVGNQLPFVGFTYY   400
 401  SNRRYLSPANPNDNRTSSNVDKSLQENLQKTIYKLEEQLHNEMQLKDEME   450
 451  QKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQR   500
 501  KAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLAQLQKQLEEA   550
 551  NDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTD   600
 601  KDYYQLQAVLEAERRDRGHDSEMIGDLQARITSLQEEVKHLKYNLERMEG   650
 651  ERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTD   700
 701  KHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQ   750
 751  SQQKLEHLTENKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNL   800
 801  KGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQ   850
 851  YFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAE   900
 901  SEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHAVSRLEETNSILTK   950
 951  DIELLRKENEELTDKMRKSEEEYKLQKEEEISNLKATYEKNINTERTLKT  1000
1001  QAVNKLAEIMNRKDFKIDKKKANTQDLRKKEKENRKLQLELNQEREKFNQ  1050
1051  MVVKHQKELNDMQAQLVEECTHRNELQMQLASKESDIEQLRAKLSDLSDS  1100
1101  TSVASFPSADETDPNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKI  1150
1151  LFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILY  1200
1201  ANEGECRKDVEVEPVQQAEKTNFQNHKGHEFIPTLYHFPANCEACAKPLW  1250
1251  HVFKPPPALECRRCHVKCHRDHLDKKEDLISPCKVSYDVTSARDMLLLAC  1300
1301  SQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTS  1350
1351  GKTS                                                1354

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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