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

NucPred

Fetching Q01879 from www.uniprot.org...

The NucPred score for your sequence is 0.90 (see score help below)

   1  MSSFESDSASDAESAFSDASSDFTPSSSVKSKGKVPLRDSTNTTAQPSAP    50
  51  ATGDASDQYQKLSQLEHILKRPDTYIGSVEKTGVEMWSFDAATESMIYKE   100
 101  VHIVPGLYKIFDEILVNAADNKIRDPSMRNIKVNIDAENNTISVMNDGKG   150
 151  IPIEIHKKEKIYIPELIFGNLLTSSNYDDDEKKVTGGRNGYGAKLCNIFS   200
 201  TEFVLETADLNTSQVYKQTWTNNMANLTKPKITKLKSKKEYTKVTFKPDL   250
 251  SKFHMEMLDDDILSVLRRRVYDLCGTVKDCNIYLNDKKLSIRNFKSYVEM   300
 301  YVNAIRERSPDPVAPEGETRNYSTIVHEVFNDRWEVAFAVSDGSFNQVSF   350
 351  VNSIATTSGGTHVKYVSDQIITKLIDALTKKEKGKKKLNIKPQEVRNNMF   400
 401  VFINCLIENPAFTSQTKEQLTTRVAQFGGKPSEKIVISDQFIAKILKTSI   450
 451  ADKIRTIVNANEDKEMSKADGSRKSRIKNQVKLVDANKAGTKEGYKCTLI   500
 501  LTEGDSAMPLAVAGLTVVGRDYYGCFPLRGKLLNVREASIEQVSKNAEIN   550
 551  SIKQIMGLQHKKRYTPENIKSLRYGHIMIMTDQDQDGSHIKGLIINFLET   600
 601  SFPGLLEIPGFLLEFITPIVKVTITGRGNHRVIPFYNMPEFEKWRETEGT   650
 651  TCKWKHKYFKGLGTSLESEGREYFAALDRHMKSFHALQDGDSQYIDMAFS   700
 701  KKKADERKDWLQAFRPGTHLDPQIREIPISEFINKELILFSMADNVRSIP   750
 751  SILDGFKPGQRKVLYGCFKRNLRSEIKVAQLVGYISEHTGYHHGEGSLAQ   800
 801  TIVGLAQNFVGANNLNLLQPIGLFGSRAAGGKDFSAVRYIHTNITDLTRV   850
 851  IFNPLDDDLYTYVQDDAQTVEPEAYLPIIPMLLVNGAEGIGTGWSTNIPS   900
 901  FNPVDIVANIRRMMNGEEPVDMTPWFKGWEGDLERISPEKYKVSGKIEQV   950
 951  DDDTVEITEIPIKTWTNSVKEFLLAGLGNDKPWIKDMEEQHGINIRFVVR  1000
1001  LSKEEMDKSLRMGLLERFKLISSISLSNMVAFDPQGRIKKYSSANEILKD  1050
1051  YYWARLELYQRRKDMMAENFQNQLTRLSEQARFIKLIIEKKLTIANKKRS  1100
1101  EMVDDLKKLKFTRFNKNGKPVHDEPLVEAEELAEEEEEAAGDISQLNLGL  1150
1151  VAPEDESQYKPETTYSQYDYLLGMAIWSLTRERYEKLLRQRDEKQEELNE  1200
1201  LLKKSAKDLWNSDLDDFLVGWEEFLRADIEARNSFGPTAKTSTRKRARKS  1250
1251  TKSEPAQKKTKSSTPKASTPTIKAEATPAQPVVKEETNKQPDLLSFFSKE  1300
1301  PSVAKTVSAAPPKRKTPKSKPKKEIVSLFSDSSDDDITSFSVGDAKPTPK  1350
1351  PSTNNILDELEDLKSSTFTPKVGAAGRRRPKSYALAESDGNESDEDYMSE  1400

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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