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

NucPred

Fetching Q5XPJ9 from www.uniprot.org...

The NucPred score for your sequence is 0.71 (see score help below)

   1  MPLTRYQSRNEYGLADPDLYQAADKDDPEALLEGVAMAGLVGILRQLGDL    50
  51  AEFAAEMFHDLHEEVMATASRSHGLMARVQQLEAEFPSIEKALLCQTDHS   100
 101  PFFSNKGVEWHPNLQLEQSVVTSGDLPRCVMDSYEECRGPPRLFLLDKFD   150
 151  ISGAGACLKRYTDPSFVRLETSSYEESWDDIQREKKSQKAKRRASQWRNG   200
 201  GTPENALSSHAKLHELFLEEHLEAHHSDPARVVKLKTRKLDGCSLISKSG   250
 251  ESYMEKFVQTRVDSKISYEIITQNPGLLTWNMDSARDVVTDIPEISMVGA   300
 301  MDKSHGGSRAEVSFPSEQENVANVNMNGGFIEKDIETVPESTYNEVRGTT   350
 351  ITQDSQTVLNGKPGFFQQRSYSEDLTSEADNYVDAPATMESETETDDECR   400
 401  PKSRSDTLKDGNHHIYSDAVEERMEDPPQFSFSHSNGNTPVSENGRSSFG   450
 451  KKSTSYSYSDTASISIDDQSDGEKLSGCLPSTSSFKSELVDSMSHVTPEA   500
 501  NKVSHDLNVQESVSSSNVDGQTSLSSNGTCSSPRPVSQNDQSCSLTVQSL   550
 551  ASEVVETSPELVRLDLMKGGNDGRKVDPFDSSKSCASFDAKNSDLPSETS   600
 601  SISSTSEGSRCDSTIEKNCMVASNLVNSGTSPQAFVDSQTGKQLPIADTD   650
 651  FETNSIVACSEVLANSGSDPEERDGRCLTGKLVPCSAGVGMEVSPDTPSK   700
 701  VCGPSSADGIHLKDTLDDETDCVSVTNVVVDVDSKNSVADVGSQSSVADI   750
 751  DSQSSVAEISDEHSCAFGNTADVSVSESHEDTLENGMSVPSDFNSGVEKL   800
 801  AGDASPTCSKCDDHISHEGFHDLSGLDNATTDIVPNVELDVSDNDNDTSS   850
 851  GGVNHAVSLSSTRGKGSLPWISTNTYQSSSDAGEIFHDTVVESDGTLLED   900
 901  NNPESEIKMHKSPLEVSSEGLSTEPDNKDVESIESTSPKPSLDQRNRDTE   950
 951  TKSPGESILDDNCIDSTQVYNLNLLESEAIDQAVREQTSYASHEVADEEL  1000
1001  LQSNVFRGLEFEPQSAGLEFAPQSAGIELNRPKQELNLDPTFPSFGFIPE  1050
1051  TIPPNPEDMPPLPPMQWLIGKVPHSFPTFMGESVETSSSALSAAPPIGSS  1100
1101  LNVQIGSPPSELSVSLGSDESERLPGGFVHNASEKPLQSSIQFPTMSTDL  1150
1151  NSQYDSSELPTIPYQECIEDFGSEENNLLADHAAQNHELVYSQASSLQLP  1200
1201  QVKHEDFKDDADVHESQSSSDDHHCPETKSLTPTQSTKVEDKGHSVPDAS  1250
1251  NAETAESSNTSVQKINPVSVGDAMWPVSCFSVAPTLDTYKTEVVPTVRLP  1300
1301  RPRSPLVDAVAAHDRRKMKKVSEMVHPPIKSKQDDKDSLLAQIRNKSVNL  1350
1351  KPAVTTRPSIQTGPRTDLRVAAILEKANTIRMAMAGSDEDEDSDSWSDS   1399

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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