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

NucPred

Fetching Q14112 from www.uniprot.org...

The NucPred score for your sequence is 0.30 (see score help below)

   1  MEGDRVAGRPVLSSLPVLLLLPLLMLRAAALHPDELFPHGESWGDQLLQE    50
  51  GDDESSAVVKLANPLHFYEARFSNLYVGTNGIISTQDFPRETQYVDYDFP   100
 101  TDFPAIAPFLADIDTSHGRGRVLYREDTSPAVLGLAARYVRAGFPRSARF   150
 151  TPTHAFLATWEQVGAYEEVKRGALPSGELNTFQAVLASDGSDSYALFLYP   200
 201  ANGLQFLGTRPKESYNVQLQLPARVGFCRGEADDLKSEGPYFSLTSTEQS   250
 251  VKNLYQLSNLGIPGVWAFHIGSTSPLDNVRPAAVGDLSAAHSSVPLGRSF   300
 301  SHATALESDYNEDNLDYYDVNEEEAEYLPGEPEEALNGHSSIDVSFQSKV   350
 351  DTKPLEESSTLDPHTKEGTSLGEVGGPDLKGQVEPWDERETRSPAPPEVD   400
 401  RDSLAPSWETPPPYPENGSIQPYPDGGPVPSEMDVPPAHPEEEIVLRSYP   450
 451  ASGHTTPLSRGTYEVGLEDNIGSNTEVFTYNAANKETCEHNHRQCSRHAF   500
 501  CTDYATGFCCHCQSKFYGNGKHCLPEGAPHRVNGKVSGHLHVGHTPVHFT   550
 551  DVDLHAYIVGNDGRAYTAISHIPQPAAQALLPLTPIGGLFGWLFALEKPG   600
 601  SENGFSLAGAAFTHDMEVTFYPGEETVRITQTAEGLDPENYLSIKTNIQG   650
 651  QVPYVSANFTAHISPYKELYHYSDSTVTSTSSRDYSLTFGAINQTWSYRI   700
 701  HQNITYQVCRHAPRHPSFPTTQQLNVDRVFALYNDEERVLRFAVTNQIGP   750
 751  VKEDSDPTPGNPCYDGSHMCDTTARCHPGTGVDYTCECASGYQGDGRNCV   800
 801  DENECATGFHRCGPNSVCINLPGSYRCECRSGYEFADDRHTCILITPPAN   850
 851  PCEDGSHTCAPAGQARCVHHGGSTFSCACLPGYAGDGHQCTDVDECSENR   900
 901  CHPAATCYNTPGSFSCRCQPGYYGDGFQCIPDSTSSLTPCEQQQRHAQAQ   950
 951  YAYPGARFHIPQCDEQGNFLPLQCHGSTGFCWCVDPDGHEVPGTQTPPGS  1000
1001  TPPHCGPSPEPTQRPPTICERWRENLLEHYGGTPRDDQYVPQCDDLGHFI  1050
1051  PLQCHGKSDFCWCVDKDGREVQGTRSQPGTTPACIPTVAPPMVRPTPRPD  1100
1101  VTPPSVGTFLLYTQGQQIGYLPLNGTRLQKDAAKTLLSLHGSIIVGIDYD  1150
1151  CRERMVYWTDVAGRTISRAGLELGAEPETIVNSGLISPEGLAIDHIRRTM  1200
1201  YWTDSVLDKIESALLDGSERKVLFYTDLVNPRAIAVDPIRGNLYWTDWNR  1250
1251  EAPKIETSSLDGENRRILINTDIGLPNGLTFDPFSKLLCWADAGTKKLEC  1300
1301  TLPDGTGRRVIQNNLKYPFSIVSYADHFYHTDWRRDGVVSVNKHSGQFTD  1350
1351  EYLPEQRSHLYGITAVYPYCPTGRK                           1375

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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