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

NucPred

Fetching Q8K2Z4 from www.uniprot.org...

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

   1  MSPHNFEFHLPLSPEELLKSGGVNQYVVREVLPVKHLSSQLRAFQSAFRA    50
  51  QGPLAILEHFDTVYSILHHFRSIEPGLKEDTLEFLKKVVSRHSQELSSIL   100
 101  DDAALSGSDRSAHLNALKMNCYALIRLLESFENMTSQTSLIDLDIGGKGK   150
 151  RARAKATLGFDWEEERQPVLQLLTQLLQLDIRHLWNHSAIEEEFVSLVTG   200
 201  CCYRLLENPTISHQKNRSTKEAIAHLLGVALVRYNHMLSATVKIIQMLQH   250
 251  FEHLPPVLVTAVSLWATDYGMKSIVGEIVREIGQKCPQELSRDTAGAKGF   300
 301  AAFLTELAERIPAVLMANMCILLDHLDGENYMMRNAVLAAIAEMVLQVLN   350
 351  GDQLEESARETRDQFLDILQAHGHDVNSFVRSRVLQLFARIVQQKALPLT   400
 401  RFQAVVALAVGRLADKSVLVCKNAIQLLASFLANNPFSCKLSDIDLAGPL   450
 451  QKEIQKLQEMRAQRRSAAATAALDPEEEWDAMLPELKSTLQQLLKLPQEE   500
 501  GDHQIADAETAEEVKGRIRQLLAKASYKQAIVLTREATSHFQESEPFSHT   550
 551  EPEENSFLNLLGLIFKGPEASTQDSHGDTDPGLTGSKDSPSVPEPEGSQS   600
 601  NDELVKQEMLVQYLQDAYGFSQKITEAIGIISKMMYENTTTVVQEVIEFF   650
 651  VMVFQFGVPQALFGVRRMLPLIWSKEPGVREAVLNAYRQLYLNPKGDSAR   700
 701  AKAQTLIHNLSLLLVDASVGTIQCLEEILCEFVQKDEVKPAVIQLLWERA   750
 751  TEKVPSSPLERCSSVMLLGMMARGKPEIVGSNLDALVRVGLDEKSPQDYR   800
 801  LAQQVCLAIANISDRRKPSLGERHPPFRLPQEHRLFERLQDMVTKGFAHP   850
 851  DPLWIPFKEVAVTLTYQLAESPDVLCAQMLQGCAKQVLEKLEKNATEADP   900
 901  KETAPRLPTFLLMNLLSLAGDVALQQLVHLEQAVSGELGRRRVLREEQEH   950
 951  RAKEPKEKTASSETTMEEELGLVGGATADDTEAELIRSICEKELLDGNQV  1000
1001  LAAFVPLLLKVCNNPGLYSNPELCAAASLALGKFCMISAPFCDSQLRLLF  1050
1051  TMLEKSSLPTVRSNLMVATGDLAIRFPNLVDPWTPHLYARLRDPAQQVRK  1100
1101  TAGLVMTHLILKDMVKVKGQVSEMAVLLIDPVPQIAALAKNFFNELSHKG  1150
1151  NAIYNLLPDIISRLSDPEGGVEEEPFHTIMKQLLSYITKDKQTESLVEKL  1200
1201  CQRFRTARTERQYRDLAYCMSQLPLTERGLQKMLDNFECFGDKLLDESVF  1250
1251  SAFLSIVGKLRRGAKPEGKAIIDEFEQKLRACHTRGMDGIEEFETGQGGS  1300
1301  QRALSAKKPSAVSRLQPLTSVDSDNDFVTPKPRRTKPGRPQTQQRKKSQR  1350
1351  KAKVVFLSDESSEDELSAEMTEEETPKRTTPIRRASGRRHRS          1392

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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