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

NucPred

Fetching P12080 from www.uniprot.org...

The NucPred score for your sequence is 0.49 (see score help below)

   1  MSGDSIHRRRMALHCPITSLILLLIAMSAHGYNIDLPSYVRFRQSSNSMF    50
  51  GFSIAMHKGRSGFYGNQNNVSLIVGAPKFDTSRYQQGVTEAGGVFKCSLN   100
 101  DDDCKLVPFDSKGNNRNVDKEVVDRKSYQWLGATVATGRDSDLVVACAPR   150
 151  YVFHTMTPSRAFRIDPVGTCFTSHNFEEFYEVSPCRTNNWGYHRQGSCQA   200
 201  GFSAAINGNGSRLFIGAPGSWYWQGQTYSIPPDAKFPFKPPLYQPFGTGG   250
 251  MASSHDVTRPENQVFSTSESASVNDDSYLGYSMVTGDFDGDRSEDVAIGM   300
 301  PRGGNLVGRIVVNRWNMANIFNITGRQIGEYFGYSLATSDVDGDGLDDLL   350
 351  IGAPMYTDPDNVEGKYDVGRVYILLQGGPTEEKRWTTEHIRDGYHSKGRF   400
 401  GLALTTLGDVNGDGYGDFAVGAPYDGPEGRGVVYIFHGSPMGPLAKPSQI   450
 451  IKSEQLVEGAPYPRTFGFALSGGLDMDGNTYPDLAVGAYSSDQVFIFKSR   500
 501  PVAAVNAETSFASNSKLISLDDRSCQLVRDHKKVPCMLLTTCWSYTGRYL   550
 551  PEQLDFDVSWLLDAKKLLNPRMFFLRDEGKNIRNQTIRLNYGQKYCLNET   600
 601  VYLLDKVQDKLTPLEVEARYNLRSSRPLDPMVRHRRSILEPVIDQNREIV   650
 651  LRDAINIQKNCGPDNICEPDLKLKVSTVDKYLFGSPEPLVIEVFISNTNE   700
 701  DAFEAAFYMVTPPDLQFRKLQQLGEKKDTPITCSPPTPENNHTLKCDIGN   750
 751  PLESGKIAHFKISLVPEEKYGSSSSYDFYWEANSTNLEKPGSEYDNKIRQ   800
 801  SVGIWVDTDLDIKGTSLPDYQLYKADDYKELENATKEDDIGPQVVHIYEI   850
 851  RNNRPSIIEEAEVFIHLPYETIVGDPLMYLLNQPETGGKIQCDDVAFNEY   900
 901  NLLLDEKLVKKSYLQAQGAIWNSAQVSGQSSSSSSSGGASVHIEKARGEG   950
 951  FVRGVLVSNSTDAGDKLSPKQVEQRRQEDTLEALGDASFVHRDRASQAVQ  1000
1001  EPQVNQTSFTTYSTSSSSSGSGAPSAQLRGHSTQGHIQMAGPVQHTSSSS  1050
1051  SSNYRSWPAQQQQQHQQLLLAGSGGSGLGSPVTFNDKSQFGGRNNNFHTG  1100
1101  TLDLGTLNRGNVDNELYRSQGQYQNPSQSLGQSQGQFQANANQGHYQGQN  1150
1151  QAQFQARNPGFQGQTSYQGQTQYSGQPGGYQTHHVTYSSGSKPYYGRENE  1200
1201  DFYDEDNLQQATPGHWSSSSSSSSSSGTRRLRRSNDKDGATEKPLQIDLN  1250
1251  SPCQSARCKSIRCVVTNLGTEDGDAAFVAIRARMVAKTMEKLASNVPLNV  1300
1301  STLAVANVTLLPFIGAPKDAIVKTHEIFYKAEPEPLQVPDVVPLWVVVLA  1350
1351  ACAGALIFLLLVWLLYKCGFFNRNRPTDHSQERQPLRNGYHGDEHL      1396

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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