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

NucPred

Fetching O02466 from www.uniprot.org...

The NucPred score for your sequence is 0.44 (see score help below)

   1  MRVVDKMAGLMWAALTLVIGLGLLVPSNGEEYICDSMDIRNRVSNLRQLE    50
  51  NCTVIEGYLQILLIDFAEEQDYSGLAFPNLVEITDYFLLYRVRGLTNLSE   100
 101  LFPNLAVIRGTNLFFNYALVVFEMLDMQKIGLYSLQNITRGSVRIEKNPN   150
 151  LCYLDTIDWSFIAESGYSNNFIVDNREEEECVNFCPGRCRIKHPVLQDLC   200
 201  WAEEHCQKVCPESCLGNCRDGISGCCHENCIGGCDGPTERDCVACKYFVH   250
 251  NGECLIQCPPDTYQYKDRRCITEEECPNTTNSVWKLHHRKCIPECPSGYT   300
 301  TDINNPRLCTECEGQCPKSCKGGLVDSLAAAQRFRGCTIIEGELKISIRG   350
 351  GDNIIDELEENLGLIEEVGHYVAIVRSYALVTLDFLRSLKRIRGIQKENG   400
 401  YAFYVLDNRNLEKLFDWDRTDITIDEGKLFFHFNPKLCRHVILTMVDKVG   450
 451  LPEHAITDTDISTLTNGDQAQCSFSRLEIEEINTSKDMIILRWSEFRPPD   500
 501  PRDLLSYTVSYRETEDQGIDEYDGQDACGNTEWKEFDVSPTQTAHIITGL   550
 551  KPWTQYALLVKTYTKAGAREGSGAKSDIVYARTDADKPTHPQDVVVYSNS   600
 601  SNTLIITWKPPNRPNGNVTHYIVKYKRQQEDVAEMEQREYCKGGLKPHRP   650
 651  TQGLEDIVNNEEEPNNSTIGDGTCCECPKSEDEIRIEEEEAAFQQEFENF   700
 701  LHNNVYHKRENETRAGRRRRELPVTARPFYSNQTVNVTLPSTNRTVPPTP   750
 751  TPNPNPQLETTVWNEHMVVLTGLRHFSEYIIEVIACNADAAVGCSGSAVE   800
 801  LARTQADDSADNIPGNITVVEIKEDMAKLYWPKPDSPNSMVVHYNIEYKK   850
 851  LGSDFNYEQQEPKCVENFKFLQSQGYTISNLVAGNYSVRFRATSFAGNGS   900
 901  WSNYVTFYVEEEDTSPDPQDPQQQVPVSLMIGMGVGFSLLLILAVIFGIW   950
 951  YCTKKRFGDKQMPNGVLYASVNPEYMSSDDVYVPDEWEVPREKITLIREL  1000
1001  GQGSFGMVYEGEAKDVVKDEPMVSVAVKTVNESASIRERIEFLNEASVMK  1050
1051  TFNCHHVVKLMGVVSKGQPTLVVMELMALGDLKNYLRRHRPEEDVGLSDS  1100
1101  PASNEAKNSPFAENDNDLPPTFKDIIQMAGVIADGMSYLAAKKFVHRDLA  1150
1151  CRNCMVAQDRTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKVG  1200
1201  VFTSQSDVWSYGVVLWEMATLASQPYQGKSNEEVLKFVIDGGMLEKPEGC  1250
1251  PNKLYDLMKLCWQYRQSMRPTFLEIVEILSPELQAHFNEVSFYHSLDNHG  1300
1301  REPLEMDDVALDSGADTETEMYPSGSEFSSTPSPPSETPYSHMNGSHPQN  1350
1351  GSMNLRIPKSTLC                                       1363

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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