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

NucPred

Fetching P28666 from www.uniprot.org...

The NucPred score for your sequence is 0.47 (see score help below)

   1  MWKSRRAQLCLFSVLLAFLPSASSLNGDSKYMVLVPSQLYTETPEKICLH    50
  51  LYHLNETVTVTASLVSQTGRRNLFDELVVDKDLFQCVSFIIPTLNSPDEE   100
 101  EFLYVDIKGPTHEFSKRNAVLVKNKESVVFVQTDKPVYKPGQSVKFRVVS   150
 151  MDKTLRPLNELLPLAYIEDPKKNRIMQWRDIKTENGLKQMSFSLAAEPIQ   200
 201  GPYKIVVHKQSGVKEEHSFTVMEFVLPRFNVDLKVPNAISVNDEVLQVTV   250
 251  CGKYTYGKPVPGQVKISICHETEAGCKEVNSKLDNNGCSTQEVNITELQS   300
 301  KKRNYEVQLFHVNATVTEEGTGLEFNGYGTTKIERITNKLIFLKADSHFR   350
 351  HGIPFFVKVRLVDIKGDPIPNERVFIKAQVLGYTSATTTDQHGLAKFSID   400
 401  TAGFSGSSLHIKVNHKGKDSCYFFYCMEERYASAEHVAYAVYSLSKSYIY   450
 451  LVKETSSILPCNQIHTVQAHFILKGDLGVLKELVFYYLVMAQGSIIQTGN   500
 501  HTHQVEPGEAPVKGNFDLEIPVEFSMAPMAKMLIYTILPDGEVIADSVNF   550
 551  EIEKCLRNKVDLSFSSSQSLPASQTRLQVTASPQSLCGLRAVDQSVLLLK   600
 601  PEDELSPSWIYNLPGMQHNKFIPSSSLSEDREDCILYSSWVAEKHTDWVP   650
 651  HGREKDVYRYVEDMDLKAFTNLKIKLPKICFDSAPMSGPRGKFDLAFSSE   700
 701  VSGTLQKGSSKRPQPEEPPREDPPPKDPLAETIRKYFPETWVWDIVTVNS   750
 751  TGVAEVEMTVPDTITEWKAGALCLSNDTGLGLSSVVPLQAFQPFFVEVSL   800
 801  PYSVVRGEAFMLKATVMNYLPTSMRMSVQLEASPDFTAVPVGDDHDSYCL   850
 851  SANGRHTSSWLVTPKSLGNVNFSVSVEAQQSSEPCGSEVATVPETGRKDT   900
 901  VVKVLIVEPEGIKQEHTFNSLFCASDAEISEKMSLVLPPTVVKDSARAHF   950
 951  SVMGDILSSAIKNTQNLLHMPYGCGEQNMVLFAPNIYVLKYLDKTQQLTQ  1000
1001  KIKTKALGFLRAGYQRELNYKHKDGSYSAFGDQNGEREGNTWLTAFVLKS  1050
1051  FAQARAFIFIDESHITHAFTWLSQQQKDNGCFRSSGSLFHNDIKHPVVSK  1100
1101  ALSCLESSWKTIEQGRNANFVYTKALMAYAFALAGNQDKRNEILKSLDEE  1150
1151  AIKEDNSIHWERPQKPRKSEHNLYKPQASSVEVEMNAYVVLARLTAQPAP  1200
1201  SPEDLTLSRSTIMWLTKQQNSNGGFSSTQDTVVALDALSKYGAVTFSRRQ  1250
1251  KTSLVTIQSTGSFSQKFQVENSNCLLLQQVPLPDIPGDYTISVSGEGCVY  1300
1301  AQTTLRYNMHLEKQQSAFALRVQTVPLTCNNPKGHNSFQISLEISYTGSR  1350
1351  PASNMVIADVKMLSGFIPLKPTVKKLERLEHISRTEVSNNNVLLYLDQVT  1400
1401  NQTLAFSFIIQQDISVRNLQPAIVKVYDYYETDEVAYAEYSSPCSSDKQN  1450
1451  V                                                   1451

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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