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

NucPred

Fetching O88700 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MAAVPLNNLQEQLQRHSARKLNNQPSLSKPKSLGFTFKKKTSEGDVSVTS    50
  51  VSVVKTPALSDKDVNVSEAFSFTESPLHKPKQQAKIEGFFKHFPGRQQSK   100
 101  GTCSEPSLPATVQTAQDTLCTTPKTPTAKKLPVAVFKKLEFSSSADSLSD   150
 151  WADMDDFDMSASDAFASLAKNPATRVSTAQKMKKTKRNFFKPPPRKANAV   200
 201  KTDLTPPSPECLQVDLTKESEEEEEEEEEAEGADCLSRDVICIDNDSASE   250
 251  ELTEKDTQESQSLKAHLGAERGDSEKKSHEDEAVFHSVQNTEYFEHNDND   300
 301  YDIDFVPPSPEEIISTASSSLKCSSMLKDLDDSDKEKGILSTSEELLSKP   350
 351  EEMTTHKSDAGTSKDCDAQQIRIQQQLIHVMEHICKLVDTVPTDELEALN   400
 401  CGTELLQQRNIRRKLLAEAGFNGNDVRLLGSLWRHRPDSLDNTVQGDSCP   450
 451  VGHPNKELNSPYLLSHSPSTEECLPTTTPGKTGFSATPKNLFERPLLNSH   500
 501  LQKSFVSSNWAETPRMENRNESTDFPGSVLTSTTVKAQSKQAASGWNVER   550
 551  HGQASYDIDNFNIDDFDDDDDDDDWENIMHNFPASKSSTATYPPIKEGGP   600
 601  VKSLSERISSAKAKFLPVVSTAQNTNLSESIQNCSDKLAQNLSSKNPKHE   650
 651  HFQSLNFPHTKEMMKIFHKKFGLHNFRTNQLEAINAALLGEDCFILMPTG   700
 701  GGKSLCYQLPACVSPGVTIVISPLRSLIVDQVQKLTSFDIPATYLTGDKT   750
 751  DSEAANIYLQLSKKDPIIKLLYVTPEKVCASNRLISTLENLYERKLLARF   800
 801  VIDEAHCVSQWGHDFRQDYKRMNMLRQKFPSVPVMALTATANPRVQKDIL   850
 851  TQLKILRPQVFSMSFNRHNLKYYVLPKKPKKVAFDCLEWIRKHHPYDSGI   900
 901  IYCLSRRECDTMADTLQREGLAALAYHAGLSDSARDEVQHKWINQDNCQV   950
 951  ICATIAFGMGIDKPDVRFVIHASLPKSMEGYYQESGRAGRDGEISHCVLF  1000
1001  YTYHDVTRLKRLIMMEKDGNYHTKETHVNNLYSMVHYCENITECRRIQLL  1050
1051  AYFGEKGFNPDFCKKYPDVSCDNCCKTKDYKTKDVTDDVKNIIRFVQEHS  1100
1101  SSPGTRNIGPAGRFTLNMLVDIFLGSKSAKVKSGIFGKGTTYSRHNAERL  1150
1151  FKKLILDKILDEDLYINANDQPIAYVMLGTKAHSVLSGHLKVDFMETENS  1200
1201  SSIKKQKALVAKVSQREEVVKKCLGELTEVCKLLGKVFGVHYFNIFNTAT  1250
1251  LKKLAESLSSDPEVLLQIDGVTEDKLEKYGAEVIPVLQKYSEWTVPAEDG  1300
1301  SPGARGAPEDTEEEEEEAPVSSHYFANQTRNERKRKKMSATHKPKRRRTS  1350
1351  YGGFRAKGGSTTCRKTTSKSKFYGVTGSRSASCASQATSSASRKLGIMAP  1400
1401  PKPVNRTFLRPSYAFS                                    1416

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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