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

NucPred

Fetching Q80T14 from www.uniprot.org...

The NucPred score for your sequence is 0.63 (see score help below)

   1  MGVLKAWLGVALALAEFAVLPNCEGACLYQGSFLADATIWKPDSCQNCRC    50
  51  HGDIVICKPVVCKNPRCAFEKGEVLWIAPNQCCPQCAPRTPGSCHHEGKI   100
 101  HEHGTEWASAPCTVCSCTHGEVRCSHQQCTPLSCGPQELEFLAEGRCCPI   150
 151  CVGTGKPCSYDGHVFQDGEDWQLSRCAKCVCRNGLTQCFAAQCQPLFCNQ   200
 201  DEIVVRVPGKCCSQCSARSCSTAGQVYEHGEQWKEDACTLCMCDQGQVRC   250
 251  HKQVCPPLRCAKGQGRARHHGQCCEECATPDRSCSSGGVLRYQDEMWKGS   300
 301  ACEFCMCDQGQVTCQTGECAKVACALGEELVHLEGKCCPECISRNGYCIY   350
 351  EQKAETMSSSAREIKHVPDGEKWEEGPCKLCECREAQVTCYEPSCPPCPV   400
 401  ATLALVVKGQCCPDCTPVHCHPDCLTCSHSPDHCDLCQDPTKLLQNGRCV   450
 451  HSCGLGFYQAGSLCLACQPQCSTCTNGLECSSCLPPLLMQQGQCVSTCGD   500
 501  GFYQDHHSCAVCHESCAGCWGPTEKHCMACRDPLQVLRDSSCENTCGNGF   550
 551  YNRQGTCVACDQSCKSCGPSSPRCLSCAEKTILHDGKCISECPHGYYADS   600
 601  TGSCKVCHSSCASCSGPTAAHCIACIHPQTLRQGHCLPSCGEGFYPDHGI   650
 651  CEACHASCHTCVGPQPSHCTQCKKPEAGLLVEQHSGENVPYGKCVSRCGT   700
 701  HFYLESTGLCEVCHPSCLTCEGKSPHNCTGCESTHALLAGCCVSQCPETH   750
 751  FNLEGTCTECHPSCRQCHGPLESDCVSCHPHLTLTSGHCKTSCKEEQFLN   800
 801  LVGYCADCHPLCQHCVANLQDTGSICLKCQHARHLLLGDHCVPECPPGHY   850
 851  KERGTCKTCHSSCRSCQNGGPFSCSSCDTGLVLTHIGTCSTACFPGHYLD   900
 901  DNQVCQPCNRHCRSCDSQGSCTSCRDPSKVLLFGECQYESCTPQYYLDIA   950
 951  TKTCKECDWSCNACTGPLRTDCLQCMDGYVLQDGVCVEQCSPQHYRDSGS  1000
1001  CKRCDSHCVECQGPHECTRCEEPFLLFQAQCVQECGKGYFADHAKHRCIA  1050
1051  CPQGCLRCSHKDRCHLCDHSFFLKSGLCMPTCVPGFSGHSSNENCTDKMY  1100
1101  TPSLHVNGSLTLGIGSMKPLDFSLLNIQHQDGRVEDLLFHVVSTPTNGQL  1150
1151  LLSRNGKEVQLEKAGHFSWKDVNEKKVRFVHSKEKLRKGYFSLKISDQQF  1200
1201  FSEPQLINIQAFSTQAPYVLRNEVLHVSKGERATITTQLLDIRDDDNPQD  1250
1251  VVVNVLDPPLHGQLLQMPPAPAASIYQFHLDELSRGLLLYAHDGSDSTSD  1300
1301  IIVFQANDGHSFQNILFHVKNIPKNDRALRLVTNSMVWVPEGGMLKITNR  1350
1351  ILKAQAPGVRADDIIYKITHSRPQFGEVVLLMNLPADSPAGPAEEGHHLP  1400
1401  DGRMATPISTFTQQDIDDGVVWYRHLGAPTQSDSFRFQVSSATSAQEHLE  1450
1451  SHMFNIAILPQAPEAPKLSLGTSLHMTAREDGLSVIQPQSLSFVKAESPS  1500
1501  GKIIYNITVPLHPNQGIIEHRDRPHSPIQYFTQEDINQGQIMYRPPVAPP  1550
1551  HLQEIMAFSFAGLPESVKFYFTVSDGQHTSPEMALTIHLLHSDLQPPAFQ  1600
1601  VKAPLLEVSPGGRTSLGLQLLVRDAQVVPEELFFQLQKSPQHGMLVKYTA  1650
1651  KSSVTMAAGDTFTYDEVERNVLQYVHDGSSAWEDSLEISVTDGLTVTTSE  1700
1701  VKVEVSPSENRGPRLAPGSSLSMTVASQHTAIITRSHLAYVDDSSSDPEI  1750
1751  WIRLSSLPLYGVLFRSSGPDMDELSGDSNFTMEDINKKNIRYSAVFETDG  1800
1801  HSVTDGFHFSVSDMDGNHVDNQVFTITVTPAENPPHIIAFADLITVDEGG  1850
1851  RAPLSLHHFFATEDQDNLQDDAVIKLSALPKYGCIENTGTGDRFGPGANS  1900
1901  ELEASFPIQDVLENYIYYFQSVHESIEPTHDVFSFYVSDGSGRSEIHSIN  1950
1951  ITIERKNDEPPRMTLRPLGVRLSSGVAISNSSLSLQDLDTPDNELIFVLM  2000
2001  KKPDHGHLLRRSTASDPLENGTVLDQGSSFTYQDVLAGLVGYLPGDIYMA  2050
2051  VDEFRFSLTDGLHVDTGRMEIYIELPSTNIPHLAINRGLQLSAGSVARIT  2100
2101  EQHLKATDTDSEAGQVVYIMKEDPGAGRLLMAKADNLEQISVRGPIRSFT  2150
2151  QADVSQGQIEYSHGPGEPGGSFAFKFDVVDGEGNKLADQSFSIGVLEDKS  2200
2201  PPVVITNRGLVLDENSVEKITTAQLSATDQDSKPTELIYRITTQPQLGHL  2250
2251  EHVASPGIQISSFTQADLASRNVQYVRSSGTGKQSDAFSFVLSDGLHEVT  2300
2301  QTFPITIHPVDDARPLVQNRGMRVQEGVRKTITEFELKAVDVDTEAESIT  2350
2351  FTIVQPPRHGTIERTARGQRFHQTSSFTMEDIYQNRVSYSHDGSNSLKDR  2400
2401  FTFTVSDGTNPFFIIEEGGEEIMTAAPQQFHVDILPVDDGTPRIVTNLGL  2450
2451  QWLEYMDGKATNLITKKELLTVDPDTEDSQLIYEVTTGPMHGYLENKLQP  2500
2501  GRAAATFTQEHVNLGLIRYVLYEEKIQKVMDSFQFLVKDSKPNVVSDNVF  2550
2551  HIQWSLISFKYTSYNVSEKAGSVSVTVQRTGNLNQYAIVLCRTEQGTASS  2600
2601  SSHPGQQDYMEYAGQVQFDEGEGTKSCTVIINDDDVFENIESFTVGLSMP  2650
2651  AYALLGEFTQAKVVINDTEDEPTLEFDKKTYRVNESAGFLFAPIKRQGDS  2700
2701  SSTVSAVCYTVPKSAMGSSLYALESGSDFKSRGRSAESRVIFGPGVTVST  2750
2751  CDVMVIDDSEYEEEEEFEIALADASNNARIGRQAVAKVLISGPNDASTVS  2800
2801  LGNTAFTISEDAGTVKIPVIRHGTDLSTFTSVWCATRPSDPASATPGVDY  2850
2851  VPSSRKVEFGPGITEQYCTLTILDDTQYPVIEGLETFVVFLSSAQGAELT  2900
2901  KPSQAVIAINDTFQDVPSMQFSKDLLLVKEKEGVLHIPIIRSGDLSYESS  2950
2951  VRCYTQGHSAQVMEDFEERRNADSSRITFLKGQKTKNCTVYIHDDSMFEP  3000
3001  EEQFRVYLGHPLGNHWSGARIGKNSVATVTISNDEDAPTIEFEEAAYQVR  3050
3051  EPAGPEAIAVLSIKVIRRGDQNRTSKIRCSTRDGSAQSGVDYYPKSRVLK  3100
3101  FSPGVDHIFFKVEILSNEDREWHESFSLVLGPDDLVEAVLGDVTTATVTI  3150
3151  LDQEAAGSLILPAPPIVVTLADYDHVEELAKEGVKKAPSPGYPLVCVTPC  3200
3201  DPRYPRYAVMKERCSEAGINQTSVQFSWEVAAPTDGNGARSPFETITDNT  3250
3251  PFTSVNHKVLDSIYFSRRFHVRCVAKAVDKVGHVGTPLRSNVVTIGTDSA  3300
3301  ICHTPVVAGTARGFQAQSFIATLKYLDVKHKEHPNRIHISVQIPHQDGML  3350
3351  PLISTMPLHNLHFLLSESIYRHQHVCSNLVTAQDLRGLAEAGFLNDAGFH  3400
3401  STALGPGYDRPFQFDSSVREPKTIQLYRHLNLKSCVWTFDAYYDMTELID  3450
3451  VCGGSVTADFQVRDSAQSFLTVHVPLYVSYIYVTAPRGWASLEHHTEMEF  3500
3501  SFFYDTVLWRTGIQTDSVLSARLQIIRIYIREDGRLVIEFKTHAKFRGQF  3550
3551  VIEHHTLPDVKSFILTPDHLGGIQFDLQLLWSAQTFDSPHQLWRATSSYN  3600
3601  RKDYSGEYTIYLIPCTVQPTQPWVDPGEKALACTAHAPERFLIPIAFQQT  3650
3651  NRPVPVVYSLNTEFQLCNNEKVFLMDPNTSDMSLAEMDYKGAFSKGQILY  3700
3701  GRVLWNPEQNLHSAYKLQLEKVYLCTGKDGYVPFFDPTGTIYNEGPQYGC  3750
3751  IQPNKHLKHRFLLLDRSQPEVTDKYFHDVPFEAHFASELPDFQVVSSMPG  3800
3801  VDGFTLKVDALYKVEAGHQWYLQVIYIIGPDSTSRPRVQRSLTVSLRRHQ  3850
3851  RDLVDPSGWLSLDDSLIYDNEGDQVKNGTNMKSLNLEMQEPVIAASLSQT  3900
3901  GASIGSALAAIMLLLLLFLVACFVTRKCQKQKKKQPPEDTLEEYPLNTKV  3950
3951  DVAKRNADKVEKNANRQYCTVRNVNILSDNEGYYTFKGAKVKKLNLEVRV  4000
4001  HNNLQDGTEV                                          4010

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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