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

NucPred

Fetching P15398 from www.uniprot.org...

The NucPred score for your sequence is 0.69 (see score help below)

   1  MNIAQPVSSEIKSVKFGIYDVDDVEKISVKQIVNPVLLDNLNHPTNGGLY    50
  51  DLALGPYLKNSVCATCHLDERYCPGHFGHIVLPIPAYHPLFFSQMYNLLR   100
 101  STCLYCHHFKLSKVKVHLFFCRLKLLDYGLLNESEMVENVSLTEAIIKNS   150
 151  NGTPLEDGSDSEDSGLGHDDIAKDAATLMRIRDEFVAKSIADSRQNAHID   200
 201  AQLTTLLLHERKKVVRAFYHAISSRKQCDNCQSFSPNFRKEGFAKIFEIP   250
 251  LSGKNLQFMEQTGKIRSDVLRDTSKKHHEDEGYDGDSDSSNESEVEGIDL   300
 301  FEEDPNPLKNKSKSPIAHGAKYMTSTEVRNHLRRLFVKENVVLSRLYAHK   350
 351  RGKPASADMFFLQNIAVPPTRFRPASKMGDEVHENIQNELLTRILQSSIQ   400
 401  IASLSKDSTVEVNPDEKEGLERRSRAFELLINAFVQLQHDVNSLIDSNRN   450
 451  PSSGGQSRTVPPGIKQILEKKEGLFRKHMMGKRVNYAARSVISPDPNIET   500
 501  NEIGVPPVFATKLTYPEPVTLYNFNEMRNAVINGPHKWPGASHIQNEDGT   550
 551  LISLMPLTIEQRTALANQLLTPQSNLISSPYSYSRLINTNKKVYRHVRNG   600
 601  DMLILNRQPTLHKPSMMAHKARILPGEKTIRMHYANCNSYNADFDGDEMN   650
 651  MHFPQSTNARSEAQFIANTDSQYLVPTSGDPLRGLIQDHVVMGVWLTCKD   700
 701  TFYTRDEYQQLLFQALKPDETGMYGRIKTLPPAIQRPGIYWTGKQIISSV   750
 751  LLNLKPSDRPGLNLKSKAKVPGKYWSPDSEEGSVLFDDGELLCGILDKSS   800
 801  FGASAFGLVHSVHELYGPDIAGRLLSVLSRLFTAYAQMRGFTCRMDDLRL   850
 851  DEQGDNWRRQLLENGKSFGLEAASEYVGLSTDSPIALLNANLEEVYRDDE   900
 901  KLQGLDAAMKGKMNGLTSSIINKCIPDGLLTKFPYNHMQTMTVSGAKGSN   950
 951  VNVSQISCLLGQQELEGRRVPLMVSGKSLPSFVPYETSAKSGGFIASRFL  1000
1001  TGIAPQEYYFHCMAGREGLIDTAVKTSRSGYLQRCLMKHLEGLCVQYDHT  1050
1051  VRDSDGSIVQFHYGEDSLDVTKQKHLTQFEFSAKNYKSLIQKYKVKSVLS  1100
1101  AVDSETASSYAKKALKKPYKYDPVLDKYPPSRYLGSVSEKFQRAVDEYTQ  1150
1151  KNPDKLIASKKESKLDDSLLNESKFKALMQLRYQQSLVDPGESVGVLASQ  1200
1201  SIGEPSTQMTLNTFHFAGFGAKNVTLGIPRLREIIMTASANIQTPTMTLR  1250
1251  LNDGVSDKRASAFCKEVNKLVLSEVVRQVRVTEKISGQGSDEQSKTYAIR  1300
1301  LDLYSRDEYQDEYGVLQEEIESTFSNRFLKILNRIIKSYLAKSKQRKSGG  1350
1351  KDDTVPEVGQALKPLEDIDEAPIEGRAQEALEDEDNDATNEKMVSRSKQH  1400
1401  ASYEGPDEADKVALRQLKGSNKVEDVNMDEEEDEGFKSDESVSDFKERKL  1450
1451  LEKQNTVSISERRELQLKTAKEILSNCKHLDFDYVNGEWATVELVFPINT  1500
1501  EKLLMVSLVEKACSETVIHEIPGITRCFSKPPDSALDTVPKVITEGVNLK  1550
1551  AIWEFYNEISMNDIYTNDIAAILRIYGVEAARNAIVHEVSSVFGVYGIAV  1600
1601  DPRHLSLIADYMTFEGGYKAFNRMGIEYNTSPFAKMSFETTCHFLTEAAL  1650
1651  RGDVDDLSNPSSRLVVGRVGNFGTGSFDIFTPVVDSPAN             1689

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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