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

NucPred

Fetching Q7Z478 from www.uniprot.org...

The NucPred score for your sequence is 0.88 (see score help below)

   1  MGGKNKKHKAPAAAVVRAAVSASRAKSAEAGIAGEAQSKKPVSRPATAAA    50
  51  AAAGSREPRVKQGPKIYSFNSTNDSSGPANLDKSILKVVINNKLEQRIIG   100
 101  VINEHKKQNNDKGMISGRLTAKKLQDLYMALQAFSFKTKDIEDAMTNTLL   150
 151  YGGDLHSALDWLCLNLSDDALPEGFSQEFEEQQPKSRPKFQSPQIQATIS   200
 201  PPLQPKTKTYEEDPKSKPKKEEKNMEVNMKEWILRYAEQQNEEEKNENSK   250
 251  SLEEEEKFDPNERYLHLAAKLLDAKEQAATFKLEKNKQGQKEAQEKIRKF   300
 301  QREMETLEDHPVFNPAMKISHQQNERKKPPVATEGESALNFNLFEKSAAA   350
 351  TEEEKDKKKEPHDVRNFDYTARSWTGKSPKQFLIDWVRKNLPKSPNPSFE   400
 401  KVPVGRYWKCRVRVIKSEDDVLVVCPTILTEDGMQAQHLGATLALYRLVK   450
 451  GQSVHQLLPPTYRDVWLEWSDAEKKREELNKMETNKPRDLFIAKLLNKLK   500
 501  QQQQQQQQHSENKRENSEDPEESWENLVSDEDFSALSLESANVEDLEPVR   550
 551  NLFRKLQSTPKYQKLLKERQQLPVFKHRDSIVETLKRHRVVVVAGETGSG   600
 601  KSTQVPHFLLEDLLLNEWEASKCNIVCTQPRRISAVSLANRVCDELGCEN   650
 651  GPGGRNSLCGYQIRMESRACESTRLLYCTTGVLLRKLQEDGLLSNVSHVI   700
 701  VDEVHERSVQSDFLLIILKEILQKRSDLHLILMSATVDSEKFSTYFTHCP   750
 751  ILRISGRSYPVEVFHLEDIIEETGFVLEKDSEYCQKFLEEEEEVTINVTS   800
 801  KAGGIKKYQEYIPVQTGAHADLNPFYQKYSSRTQHAILYMNPHKINLDLI   850
 851  LELLAYLDKSPQFRNIEGAVLIFLPGLAHIQQLYDLLSNDRRFYSERYKV   900
 901  IALHSILSTQDQAAAFTLPPPGVRKIVLATNIAETGITIPDVVFVIDTGR   950
 951  TKENKYHESSQMSSLVETFVSKASALQRQGRAGRVRDGFCFRMYTRERFE  1000
1001  GFMDYSVPEILRVPLEELCLHIMKCNLGSPEDFLSKALDPPQLQVISNAM  1050
1051  NLLRKIGACELNEPKLTPLGQHLAALPVNVKIGKMLIFGAIFGCLDPVAT  1100
1101  LAAVMTEKSPFTTPIGRKDEADLAKSALAMADSDHLTIYNAYLGWKKARQ  1150
1151  EGGYRSEITYCRRNFLNRTSLLTLEDVKQELIKLVKAAGFSSSTTSTSWE  1200
1201  GNRASQTLSFQEIALLKAVLVAGLYDNVGKIIYTKSVDVTEKLACIVETA  1250
1251  QGKAQVHPSSVNRDLQTHGWLLYQEKIRYARVYLRETTLITPFPVLLFGG  1300
1301  DIEVQHRERLLSIDGWIYFQAPVKIAVIFKQLRVLIDSVLRKKLENPKMS  1350
1351  LENDKILQIITELIKTENN                                 1369

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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