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

NucPred

Fetching P33173 from www.uniprot.org...

The NucPred score for your sequence is 0.87 (see score help below)

   1  MAGASVKVAVRVRPFNSREMSRDSKCIIQMSGSTTTIVNPKQPKETPKSF    50
  51  SFDYSYWSHTSPEDINYASQKQVYRDIGEEMLQHAFEGYNVCIFAYGQTG   100
 101  AGKSYTMMGKQEKDQQGIIPQLCEDLFSRINDTTNDNMSYSVEVSYMEIY   150
 151  CERVRDLLNPKNKGNLRVREHPLLGPYVEDLSKLAVTSYNDIQDLMDSGN   200
 201  KPRTVAATNMNETSSRSHAVFNIIFTQKRHDAETNITTEKVSKISLVDLA   250
 251  GSERADSTGAKGTRLKEGANINKSLTTLGKVISALAEMDSGPNKNKKKKK   300
 301  TDFIPYRDSVLTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADR   350
 351  AKQIRCNAIINEDPNNKLIRELKDEVTRLRDLLYAQGLGDITDMTNALVG   400
 401  MSPSSSLSALSSRAASVSSLHERILFAPGSEEAIERLKETEKIIAELNET   450
 451  WEEKLRRTEAIRMEREALLAEMGVAMREDGGTLGVFSPKKTPHLVNLNED   500
 501  PLMSECLLYYIKDGVTRVGREDAERRQDIVLSGHFIKEEHCIFRSDSRGG   550
 551  GEAVVTLEPCEGADTYVNGKKVTEPSILRSGNRIIMGKSHVFRFNHPEQA   600
 601  RQERERTPCAETPAEPVDWAFAQRELLEKQGIDMKQEMEQRLQELEDQYR   650
 651  REREEATYLLEQQRLDYESKLEALQKQMDSRYYPEVNEEEEEPEDEVQWT   700
 701  ERECELALWAFRKWKWYQFTSLRDLLWGNAIFLKEANAISVELKKKVQFQ   750
 751  FVLLTDTLYSPLPPDLLPPEAAKDRETRPFPRTIVAVEVQDQKNGATHYW   800
 801  TLEKLRQRLDLMREMYDRAAEVPSSVVEDCDNVVTGGDPFYDRFPWFRLV   850
 851  GRAFVYLSNLLYPVPLVHRVAIVSEKGEVKGFLRVAVQAISADEEAPDYG   900
 901  SGVRQSGTAKISFDDQHFEKFQSESCPVVGMSRSGTSQEELRIVEGQGQG   950
 951  ADAGPSADEVNNNTCSAVPPEGLMDSPEKAALDGPLDTALDHLRLGSTFT  1000
1001  FRVTVLQASSISAEYADIFCQFNFIHRHDEAFSTEPLKNTGRGPPLGFYH  1050
1051  VQNIAVEVTKSFIEYIKSQPIVFEVFGHYQQHPFPPLCKDVLSPLRPSRR  1100
1101  HFPRVMPLSKPVPATKLSTMTRPSPGPCHCKYDLLVYFEICELEANGDYI  1150
1151  PAVVDHRGACMGTFLLHQGIQRRITVTLLHETGSHIRWKEVRELVVGRIR  1200
1201  NTPETDEALIDPNILSLNILSSGYVHPAQDDRVFFGNDTRTFYQFEAAWD  1250
1251  SSMHNSLLLNRVTPYREKIYMTLSAYIEMENCTQPAVITKDFCMVFYSRD  1300
1301  AKLPASRSIRNLFGSGSLRATEGNRVTGVYELSLCHVADAGSPGMQRRRR  1350
1351  RVLDTSVAYVRGEENLAGWRPRSDSLILDHQWELEKLSLLQEVEKTRHYL  1400
1401  LLREKLETTQRPGPEVLSPASSEDSESRSSSGASSPLSAEGQPSPLEAPN  1450
1451  ERQRELAVKCLRLLMHTFNREYTHSHVCISASESKLSEMSVTLMRDPSMS  1500
1501  PLGAATLTPSSTCPSLIEGRYGATDVRTPQPCSRPASPEPELLPELDSKK  1550
1551  TPSPVRATETEKEPQRLLVPDIQEIRVSPIVSKKGYLHFLEPHTAGWAKR  1600
1601  FVVVRRPYAYMYNSDKDTVERFVLNLSTAQVEYSEDQQAMLKTPNTFAVC  1650
1651  TEHRGILLQANSDKDMHDWLYAFNPLLAGTIRSKLSRRRSAQMRV       1695

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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