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

NucPred

Fetching P98093 from www.uniprot.org...

The NucPred score for your sequence is 0.36 (see score help below)

   1  AALQVLSAPNLLRVGSNENIFVESQDHVGGPLNVKIMVKNHPTQSKELAS    50
  51  KSVVLDQANNFQAMTQLVIQRGPLVDDPKQKQYVVLQAQFPDRLLEKVVL   100
 101  VSFQSGYIFIQTDKTIYTPASTVHYRVFSMTPGLEPLTREIFEDQEVAKN   150
 151  KEIAVSVEIMTPENITIFREIVNPDKGVKSGQFKLPDIVSFGTWHVVTRF   200
 201  QSTPQKTFSSEFEVKEYVLPSFEVSLTPAKAFFYVDDNDLTVDITARYLY   250
 251  GKEVTGTGYVVFGVITTESEKKSFPASLQRVEIKDGKGVACLKKEHITQT   300
 301  FPKIHDLVKQSIFVSVSVLTEGGGEMVEAEKRGIQIVTSPYSILFKRTPK   350
 351  YFKPGMPFDVSVYITNPDNSPAIGVEVEVTPDHAKGVTRANGFAKIPLNT   400
 401  VASATELVITVKTKDPGDPRQQTGGGTMKALPYRTSTKNFLHVGVDSNEL   450
 451  KIGDPIKIDLNLGPTTIPNHDLTYMFLSRGQLVKVGRFKRQGNALVTLSV   500
 501  PVSKELLPSFRIVAYYHVGAADLVADSVWVDIKVSCMGSLKVTSTRPKAS   550
 551  YEPRRAFSLTITGDPGAKVGLVAVDKGVYVLNSKHRLTQTKIWDTIEKHD   600
 601  TGCTAGGGADNMGVFYDAGLVFETNTAKGTGIRTDPSCPVSSRRRRAVTI   650
 651  SDVITSMASKYHGLAKECCVDGMRDNTMGYTCDRRAQYISDGDVCVQAFL   700
 701  VCCTEMASKKIESKQDALLLSRSEEDDDDDAYMRSEDIVSRSQFPESWMW   750
 751  EDTNLPECPAQNKHCESTSVIRNNFLKDSITTWQITAISLSKTHGICVAD   800
 801  PFEMIVLKEFFIDLKLPYSAVRNEQLEVKAILHNYSEDPIIVRVELMENG   850
 851  EVCSSASKKGKYRQEVNMDPMSTRVVPYVIIPMKLGLHSIEVKASVKNSG   900
 901  SNDGVKRDLRVVAEGVLVKKETNVLLNPVKHGGEQTSHIPSGVPRNQVPN   950
 951  SDADTLISVTAGEQTSVLVEQAISGDSLGSLIVQPVGCGEQNMIYMTLPV  1000
1001  IATHYLDNTKKWEDIGLDKRNTAIKYINIGYQRQLAYRKEDGSYAAWVSR  1050
1051  QSSTWLTAYVVKVFAMSSTLISVQENVLCTAVKWLILNTQQPDGIFNEFA  1100
1101  PVIHAEMTGNVRGSDNDASMTAFVLIAMQEASSVCEQSVNSLPGSMAKAV  1150
1151  AYLEKRLPHLTNPYAVAMTSYALANAGKLNKETLLKFASPQLDHWPVPGG  1200
1201  YQYTLEATSYALLALVKVKAFEEAGPIVRWLNKQKKVGGGYGSTQSTIMV  1250
1251  FQAVAEYWSHVKDLKDFDLNINLEVAGRASVTKWSINNKNQFHTRTDKVN  1300
1301  SIDKDLTVKASGNGEATLSVVTLYYALPEEKDSDCESFDLSVTLTKMDKT  1350
1351  SHEDAKESFMLTIEVLYKNSERDATMSILDIGLLTGFIVDTDDLNQLSKG  1400
1401  RERYIEKFEMDKVLSERGSLILYLDKVSHKLEDRISFKIHRVQEVGVLQP  1450
1451  AAVSVYEYYNQKRCVKFYHPQREGGTLSRLCLGDVCTCAEESCSMQKKGE  1500
1501  PDVQRIDKACGAGLDYVYKATVVDSKLTTHTDTYTVKIDLVIKPGTDEGV  1550
1551  EGKNRDFMGLAYCREALGLMQGKTYMIMGKSEDLHRVEDKGLLQYKYVLG  1600
1601  EQTWIEYWPSQQECTSRDYREVCLGIDEFINQITTFGCPV            1640

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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