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

NucPred

Fetching P11978 from www.uniprot.org...

The NucPred score for your sequence is 0.82 (see score help below)

   1  MPNDNKTPNRSSTPKFTKKPVTPNDKIPEREEKSNEVKTPKIPLFTFAKS    50
  51  KNYSRPSTAIHTSPHQPSDVKPTSHKQLQQPKSSPLKKNNYNSFPHSNLE   100
 101  KISNSKLLSLLRSKTSAGRIESNNPSHDASRSLASFEQTAFSRHAQQQTS   150
 151  TFNSKPVRTIVPISTSQTNNSFLSGVKSLLSEEKIRDYSKEILGINLANE   200
 201  QPVLEKPLKKGSADIGASVISLTKDKSIRKDTVEEKKEEKLNIGKNFAHS   250
 251  DSLSVPKVSAGDSGISPEESKARSPGIAKPNAIQTEVYGINEESTNERLE   300
 301  INQEKPVKLDENSANSTVASALDTNGTSATTETLTSKKIVPSPKKVAIDQ   350
 351  DKITLHDEKTLAPSKHQPITSEQKMKEDADLKRMEILKSPHLSKSPADRP   400
 401  QGRRNSRNFSTRDEETTKLAFLVEYEGQENNYNSTSRSTEKKNDMNTSAK   450
 451  NKNGENKKIGKRPPEIMSTEAHVNKVTEETTKQIQSVRIDGRKVLQKVQG   500
 501  ESHIDSRNNTLNVTPSKRPQLGEIPNPMKKHKPNEGRTPNISNGTINIQK   550
 551  KLEPKEIVRDILHTKESSNEAKKTIQNPLNKSQNTALPSTHKVTQKKDIK   600
 601  IGTNDLFQVESAPKISSEIDRENVKSKDEPVSKAVESKSLLNLFSNVLKA   650
 651  PFIKSESKPFSSDALSKEKANFLETIASTEKPENKTDKVSLSQPVSASKH   700
 701  EYSDNFPVSLSQPSKKSFANHTEDEQIEKKKICRGRMNTIITHPGKMELV   750
 751  YVSDSDDSSSDNDSLTDLESLSSGESNEIKVTNDLDTSAEKDQIQAGKWF   800
 801  DPVLDWRKSDRELTKNILWRIADKTTYDKETITDLIEQGIPKHSYLSGNP   850
 851  LTSVTNDICSVENYETSSAFFYQQVHKKDRLQYLPLYAVSTFENTNNTEK   900
 901  NDVTNKNINIGKHSQEQNSSSAKPSQIPTVSSPLGFEETKLSTTPTKSNR   950
 951  RVSHSDTNSSKPKNTKENLSKSSWRQEWLANLKLISVSLVDEFPSELSDS  1000
1001  DRQIINEKMQLLKDIFANNLKSAISNNFRESDIIILKGEIEDYPMSSEIK  1050
1051  IYYNELQNKPDAKKARFWSFMKTQRFVSNMGFDIQKSCEPVSISTSVKPH  1100
1101  VVEPEHMADAKIMPKDILQITKKPLMVKNVKPSSPPDVKSLVQLSTMETK  1150
1151  TLPEKKQFDSIFNSNKAKIIPGNGKHASENISLSFSRPASYGYFSVGKRV  1200
1201  PIVEDRRVKQLDDITDSNTTEILTSVDVLGTHSQTGTQQSNMYTSTQKTE  1250
1251  LEIDNKDSVTECSKDMKEDGLSFVDIVLSKAASALDEKEKQLAVANEIIR  1300
1301  SLSDEVMRNEIRITSLQGDLTFTKKCLENARSQISEKDAKINKLMEKDFQ  1350
1351  VNKEIKPY                                            1358

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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