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

NucPred

Fetching P28481 from www.uniprot.org...

The NucPred score for your sequence is 0.32 (see score help below)

   1  MIRLGAPQSLVLLTLLIAAVLRCQGQDAQEAGSCLQNGQRYKDKDVWKPS    50
51 SCRICVCDTGNVLCDDIICEDPDCLNPEIPFGECCPICPADLATASGKLG 100
101 PKGQKGEPGDIRDIIGPRGPPGPQGPAGEQGPRGDRGDKGEKGAPGPRGR 150
151 DGEPGTPGNPGPAGPPGPPGPPGLSAGNFAAQMAGGYDEKAGGAQMGVMQ 200
201 GPMGPMGPRGPPGPAGAPGPQGFQGNPGEPGEPGVSGPMGPRGPPGPAGK 250
251 PGDDGEAGKPGKSGERGLPGPQGARGFPGTPGLPGVKGHRGYPGLDGAKG 300
301 EAGAPGVKGESGSPGENGSPGPMGPRGLPGERGRTGPAGAAGARGNDGQP 350
351 GPAGPPGPVGPAGGPGFPGAPGAKGEAGPTGARGPEGAQGSRGEPGNPGS 400
401 PGPAGASGNPGTDGIPGAKGSAGAPGIAGAPGFPGPRGPPGPQGATGPLG 450
451 PKGQAGEPGIAGFKGDQGPKGETGPAGPQGAPGPAGEEGKRGARGEPGGA 500
501 GPIGPPGERGAPGNRGFPGQDGLAGPKGAPGERGPSGLTGPKGANGDPGR 550
551 PGEPGLPGARGLTGRPGDAGPQGKVGPSGAPGEDGRPGPPGPQGARGQPG 600
601 VMGFPGPKGANGEPGKAGEKGLAGAPGLRGLPGKDGETGAAGPPGPSGPA 650
651 GERGEQGAPGPSGFQGLPGPPGPPGEGGKQGDQGIPGEAGAPGLVGPRGE 700
701 RGFPGERGSPGAQGLQGPRGLPGTPGTDGPKGAAGPDGPPGAQGPPGLQG 750
751 MPGERGAAGIAGPKGDRGDVGEKGPEGAPGKDGGRGLTGPIGPPGPAGAN 800
801 GEKGEVGPPGPSGSTGARGAPGERGETGPPGPAGFAGPPGADGQPGAKGD 850
851 QGEAGQKGDAGAPGPQGPSGAPGPQGPTGVTGPKGARGAQGPPGATGFPG 900
901 AAGRVGPPGANGNPGPAGPPGPAGKDGPKGVRGDSGPPGRAGDPGLQGPA 950
951 GAPGEKGEPGDDGPSGLDGPPGPQGLAGQRGIVGLPGQRGERGFPGLPGP 1000
1001 SGEPGKQGAPGASGDRGPPGPVGPPGLTGPAGEPGREGSPGADGPPGRDG 1050
1051 AAGVKGDRGETGALGAPGAPGPPGSPGPAGPTGKQGDRGEAGAQGPMGPS 1100
1101 GPAGARGIAGPQGPRGDKGESGEQGERGLKGHRGFTGLQGLPGPPGPSGD 1150
1151 QGASGPAGPSGPRGPPGPVGPSGKDGSNGIPGPIGPPGPRGRSGETGPVG 1200
1201 PPGSPGPPGPPGPPGPGIDMSAFAGLGQREKGPDPMQYMRADEADSTLRQ 1250
1251 HDVEVDATLKSLNNQIESIRSPDGSRKNPARTCQDLKLCHPEWKSGDYWI 1300
1301 DPNQGCTLDAMKVFCNMETGETCVYPNPATVPRKNWWSSKSKEKKHIWFG 1350
1351 ETMNGGFHFSYGDGNLAPNTANVQMTFLRLLSTEGSQNITYHCKNSIAYL 1400
1401 DEAAGNLKKALLIQGSNDVEMRAEGNSRFTYTALKDGCTKHTGKWGKTVI 1450
1451 EYRSQKTSRLPIIDIAPMDIGGAEQEFGVDIGPVCFL 1487

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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