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

NucPred

Fetching Q9PVZ4 from www.uniprot.org...

The NucPred score for your sequence is 0.44 (see score help below)

   1  MGQGVLRGEGHPNNNPNSKVGWKSLVGIITIFMLILCDQSDGKICYSMDI    50
  51  RNNISQFSMLEDCTVIEGHLQILLMFTSKPENFRGLRFPKLTTITDYLLL   100
 101  FRVYGLESLKDLFPNLTVIRGTRLFFNYALVIFEMVHXKEIGLYNLMNIT   150
 151  RGSVRIEKNNELCYLSTIDWSIILDSVEDNYIELNRDNKEECGDVCPGTV   200
 201  KGKSKCKHTLVNGALVERCWTQDHCQKVCPSDCKGSGCLPDGQCCHPECL   250
 251  GSCRKPNDPSECTACRHFQNEGVCVTACPKGSYQFQGWRCIDFNTCQELN   300
 301  SRCQNSRDNSCPPYVIHKGECMPDCPSGYIANSTTRTCTPCAGPCPKVCT   350
 351  IFQNVKTIDSVTSAQELRGCTVINGSLIINLRGGNNIATELEANLGLIEE   400
 401  ISGYLKIRRSYALVSLSFFRKLRLIRGEVLEAGNYSFYALDNPSLRQLWD   450
 451  WHKHNLTIIHGKLFFHHNPRLCLSQIHQMEEVTGTKGRQDKNDIATKTNG   500
 501  DQASCEDNLLTFNFIKTSHDMVLLRWDAYWPPDYRDLLGFMVHYKEAPFQ   550
 551  NVTEFDGQDACGSNSWTVVDMDAPERSADGKTQSPGCLLRSLKPWTQYAV   600
 601  FVKTLVSGSDEGRTYGAKSKIIYIRTNETIPSVPLDPFSVSNSTSQIILK   650
 651  WKPPSEPNGNVTHYLVYWQEQPEDSDLYEVDYCNKGLKLPSRTWTPPTEI   700
 701  DENGNENQTEHTSVNKCCPCPKTEFQIQKEQDESAFRKTFENYLHNEVFI   750
 751  PRPVRKRRDLFGVANGTLPDPVTAPPLFNVSSTRAPDEPEPKIYSQKVWF   800
 801  KESVLISGLKHFTGYRIEIHACNHELSMGCSVAAYVNARTMPEATADKVV   850
 851  GPITYEYVEPNIIHLKWQEPKDPNGLIVLYEVHYSRVGGIEEVITCVSQK   900
 901  QYNTDKGGKLRVLTPGNYSVKIRATSLAGNGSWTEQAYFQVPDHPHSNIV   950
 951  KIITGPIIAVFLLLIVLVYCVVQKKKDAEGPAGPLYTSSNPEYLSASEVY  1000
1001  IPDEWEVPRDKINLLRELGQGSFGMVYEGIAKDIIKGEPEVRVAVKTVNE  1050
1051  SASLRERIEFLNEASVMKAFNCHHVVRLLGVVSKGQPTLVIMELMAHGDL  1100
1101  KSYLRSLRPDAENNPGRLAPTLKEIIQMAAEISDGMAYLNAKKFVHRDLA  1150
1151  ARNCMVADDYAVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDG  1200
1201  VFTAFSDVWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGSLDHPENC  1250
1251  PPRLHSLMQMCWQYNPKMRPTFLEIIDMLKDDLRPSFQDVSFYYSDENKP  1300
1301  PETDDLEIDFENMESTPLDPSSCSLRDQSSRTNIYEEHIPYTHMNGGRKN  1350
1351  GRILSLPRSSPS                                        1362

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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