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

NucPred

Fetching P35421 from www.uniprot.org...

The NucPred score for your sequence is 0.29 (see score help below)

   1  MVILRYYDVQAHSAAEEESVLRRLREEDGAVVSVRMERCYHLEYSAQAEH    50
  51  SLALDELLVWLVKQPLSKGQSLSRQPALQSTGSSQLLLEIGPRFNFSTPY   100
 101  STNCVNIFQNLGYSEVRRMETSTRYLVTFGEGSKAPEAARFVPLLGDRMT   150
 151  QCLYTEENTPKASFDEQLPERQANWHFVPVLEEGRAALERINQELGLAFN   200
 201  DYDLDYYHDLFAKELGRNPTTVELFDCAQSNSEHSRHWFFRGRMVIDGVE   250
 251  QPKSLIRMIMDTQAHTNPNNTIKFSDNSSAMVGFDHQTIVPSSVVAPGAV   300
 301  RLQSVQSDLIFTAETHNMPTAVAPFSGATTGTGGRLRDVQGVGRGGVPIA   350
 351  GTAGYCVGALHIPGYKQPYEPLDFKYPATFAPPLQVLIEASNGASDYGNK   400
 401  FGEPVISGFALSYGLNSAADASQRDEYVKPIMFSGGLGTMPATMREKLPP   450
 451  ARGQLLAKIGGPVYRIGVGGGAASSVEIQGSGDAELDFNAVQRGDAEMEN   500
 501  KLNRVVRACLDLGEQNPILAIHDQGAGGNGNVLKELVEPGFAGAVIFSKE   550
 551  FQLGDPTITALELWGAEYQENNAILCNADQRELLEKICRRERCPISFVGV   600
 601  VTGDGRVTLLEKPAPKDLEQALNASNRSEVSPFDLELKYVLGDMPKRTYD   650
 651  LKREQTPLKELSLPKGLLLDEALERVLSLVAVGSKRFLTNKVDRCVGGLI   700
 701  AQQQCVGPLQAPLADYALTTVSHFSHSGIATSIGTQPLKGLLDPAAMARM   750
 751  CVAEALSNLVFVKISELADVKCSGNWMWAAKLPGEGARMFDACKELCQIL   800
 801  EELHIAIDGGKDSLSMAAKVGGETIKSPGTLVISTYAPCPDVRLKVTPDL   850
 851  KGPGAGSKTSLLWINLENSARLGGSALAQAYAQQGKDTPNLTRSDVLGKA   900
 901  FAVTQSLLGDGLIQAGHDVSDGGLLVCVLEMAIGGLSGLRVDLSEPLAKL   950
 951  KNFDKSVEKLNRPELAVLFAEECGWVVEVLDTDLERVRSTYEKAGVPNYY  1000
1001  LGVTEGFGLDSRVVLKNGKSELLDQPLRVLYKKWERTSYELEKLQANPEC  1050
1051  AEAEYNSLEYRQAPQYRGPQNVQAELTLKRSSAPVRVAVLREEGVNSERE  1100
1101  MMACLLRANFEVHDVTMSDLLQGTASVSQYRGLIFPGGFSYADTLGSAKG  1150
1151  WAANILHNPRLLPQFEAFKRRQDVFSLGICNGCQLMTLIGFVGSAKSEVG  1200
1201  ADPDVALLHNKSQRFECRWATVKIPSNRSIMLGSMKDLVLGCWVAHGEGR  1250
1251  FAFRDEKLISHLQSEQLVTLQYVDDVGKPTELYPLNPNGSPQGIAGLCSS  1300
1301  DGRHLALMPHPERCSSMYQWPYVPSSFEVSPTQSESPWQIMFNNAYNWCV  1350
1351  KSNQ                                                1354

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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