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

NucPred

Fetching P32597 from www.uniprot.org...

The NucPred score for your sequence is 1.00 (see score help below)

   1  MLQEQSELMSTVMNNTPTTVAALAAVAAASETNGKLGSEEQPEITIPKPR    50
  51  SSAQLEQLLYRYRAIQNHPKENKLEIKAIEDTFRNISRDQDIYETKLDTL   100
 101  RKSIDKGFQYDEDLLNKHLVALQLLEKDTDVPDYFLDLPDTKNDNTTAIE   150
 151  VDYSEKKPIKISADFNAKAKSLGLESKFSNATKTALGDPDTEIRISARIS   200
 201  NRINELERLPANLGTYSLDDCLEFITKDDLSSRMDTFKIKALVELKSLKL   250
 251  LTKQKSIRQKLINNVASQAHHNIPYLRDSPFTAAAQRSVQIRSKVIVPQT   300
 301  VRLAEELERQQLLEKRKKERNLHLQKINSIIDFIKERQSEQWSRQERCFQ   350
 351  FGRLGASLHNQMEKDEQKRIERTAKQRLAALKSNDEEAYLKLLDQTKDTR   400
 401  ITQLLRQTNSFLDSLSEAVRAQQNEAKILHGEEVQPITDEEREKTDYYEV   450
 451  AHRIKEKIDKQPSILVGGTLKEYQLRGLEWMVSLYNNHLNGILADEMGLG   500
 501  KTIQSISLITYLYEVKKDIGPFLVIVPLSTITNWTLEFEKWAPSLNTIIY   550
 551  KGTPNQRHSLQHQIRVGNFDVLLTTYEYIIKDKSLLSKHDWAHMIIDEGH   600
 601  RMKNAQSKLSFTISHYYRTRNRLILTGTPLQNNLPELWALLNFVLPKIFN   650
 651  SAKTFEDWFNTPFANTGTQEKLELTEEETLLIIRRLHKVLRPFLLRRLKK   700
 701  EVEKDLPDKVEKVIKCKLSGLQQQLYQQMLKHNALFVGAGTEGATKGGIK   750
 751  GLNNKIMQLRKICNHPFVFDEVEGVVNPSRGNSDLLFRVAGKFELLDRVL   800
 801  PKFKASGHRVLMFFQMTQVMDIMEDFLRMKDLKYMRLDGSTKTEERTEML   850
 851  NAFNAPDSDYFCFLLSTRAGGLGLNLQTADTVIIFDTDWNPHQDLQAQDR   900
 901  AHRIGQKNEVRILRLITTDSVEEVILERAMQKLDIDGKVIQAGKFDNKST   950
 951  AEEQEAFLRRLIESETNRDDDDKAELDDDELNDTLARSADEKILFDKIDK  1000
1001  ERMNQERADAKAQGLRVPPPRLIQLDELPKVFREDIEEHFKKEDSEPLGR  1050
1051  IRQKKRVYYDDGLTEEQFLEAVEDDNMSLEDAIKKRREARERRRLRQNGT  1100
1101  KENEIETLENTPEASETSLIENNSFTAAVDEETNADKETTASRSKRRSSR  1150
1151  KKRTISIVTAEDKENTQEESTSQENGGAKVEEEVKSSSVEIINGSESKKK  1200
1201  KPKLTVKIKLNKTTVLENNDGKRAEEKPESKSPAKKTAAKKTKTKSKSLG  1250
1251  IFPTVEKLVEEMREQLDEVDSHPRTSIFEKLPSKRDYPDYFKVIEKPMAI  1300
1301  DIILKNCKNGTYKTLEEVRQALQTMFENARFYNEEGSWVYVDADKLNEFT  1350
1351  DEWFKEHSS                                           1359

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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