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

NucPred

Fetching P19158 from www.uniprot.org...

The NucPred score for your sequence is 0.85 (see score help below)

   1  MSQPTKNKKKEHGTDSKSSRMTRTLVNHILFERILPILPVESNLSTYSEV    50
  51  EEYSSFISCRSVLINVTVSRDANAMVEGTLELIESLLQGHEIISDKGSSD   100
 101  VIESILIILRLLSDALEYNWQNQESLHYNDISTHVEHDQEQKYRPKLNSI   150
 151  LPDYSSTHSNGNKHFFHQSKPQALIPELASKLLESCAKLKFNTRTLQILQ   200
 201  NMISHVHGNILTTLSSSILPRHKSYLTRHNHPSHCKMIDSTLGHILRFVA   250
 251  ASNPSEYFEFIRKSVQVPVTQTHTHSHSHSHSLPSSVYNSIVPHFDLFSF   300
 301  IYLSKHNFKKYLELIKNLSVTLRKTIYHCLLLHYSAKAIMFWIMARPAEY   350
 351  YELFNLLKDNNNEHSKSLNTLNHTLFEEIHSTFNVNSMITTNQNAHQGSS   400
 401  SPSSSSPSSPPSSSSSDNNNQNIIAKSLSRQLSHHQSYIQQQSERKLHSS   450
 451  WTTNSQSSTSLSSSTSNSTTTDFSTHTQPGEYDPSLPDTPTMSNITISAS   500
 501  SLLSQTPTPTTQLQQRLNSAAAAAAAAASPSNSTPTGYTAEQQSRASYDA   550
 551  HKTGHTGKDYDEHFLSVTRLDNVLELYTHFDDTEVLPHTSVLKFLTTLTM   600
 601  FDIDLFNELNATSFKYIPDCTMHRPKERTSSFNNTAHETGSEKTSGIKHI   650
 651  TQGLKKLTSLPSSTKKTVKFVKMLLRNLNGNQAVSDVALLDTMRALLSFF   700
 701  TMTSAVFLVDRNLPSVLFAKRLIPIMGTNLSVGQDWNSKINNSLMVCLKK   750
 751  NSTTFVQLQLIFFSSAIQFDHELLLARLSIDTMANNLNMQKLCLYTEGFR   800
 801  IFFDIPSKKELRKAIAVKISKFFKTLFSIIADILLQEFPYFDEQITDIVA   850
 851  SILDGTIINEYGTKKHFKGSSPSLCSTTRSRSGSTSQSSMTPVSPLGLDT   900
 901  DICPMNTLSLVGSSTSRNSDNVNSLNSSPKNLSSDPYLSHLVAPRARHAL   950
 951  GGPSSIIRNKIPTTLTSPPGTEKSSPVQRPQTESISATPMAITNSTPLSS  1000
1001  AAFGIRSPLQKIRTRRYSDESLGKFMKSTNNYIQEHLIPKDLNEATLQDA  1050
1051  RRIMINIFSIFKRPNSYFIIPHNINSNLQWVSQDFRNIMKPIFVAIVSPD  1100
1101  VDLQNTAQSFMDTLLSNVITYGESDENISIEGYHLLCSYTVTLFAMGLFD  1150
1151  LKINNEKRQILLDITVKFMKVRSHLAGIAEASHHMEYISDSEKLTFPLIM  1200
1201  GTVGRALFVSLYSSQQKIEKTLKIAYTEYLSAINFHERNIDDADKTWVHN  1250
1251  IEFVEAMCHDNYTTSGSIAFQRRTRNNILRFATIPNAILLDSMRMIYKKW  1300
1301  HTYTHSKSLEKQERNDFRNFAGILASLSGILFINKKILQEMYPYLLDTVS  1350
1351  ELKKNIDSFISKQCQWLNYPDLLTRENSRDILSVELHPLSFNLLFNNLRL  1400
1401  KLKELACSDLSIPENESSYVLLEQIIKMLRTILGRDDDNYVMMLFSTEIV  1450
1451  DLIDLLTDEIKKIPAYCPKYLKAIIQMTKMFSALQHSEVNLGVKNHFHVK  1500
1501  NKWLRQITDWFQVSIAREYDFENLSKPLKEMDLVKRDMDILYIDTAIEAS  1550
1551  TAIAYLTRHTFLEIPPAASDPELSRSRSVIFGFYFNILMKGLEKSSDRDN  1600
1601  YPVFLRHKMSVLNDNVILSLTNLSNTNVDASLQFTLPMGYSGNRNIRNAF  1650
1651  LEVFINIVTNYRTYTAKTDLGKLEAADKFLRYTIEHPQLSSFGAAVCPAS  1700
1701  DIDAYAAGLINAFETRNATHIVVAQLIKNEIEKSSRPTDILRRNSCATRS  1750
1751  LSMLARSKGNEYLIRTLQPLLKKIIQNRDFFEIEKLKPEDSDAERQIELF  1800
1801  VKYMNELLESISNSVSYFPPPLFYICQNIYKVACEKFPDHAIIAAGSFVF  1850
1851  LRFFCPALVSPDSENIIDISHLSEKRTFISLAKVIQNIANGSENFSRWPA  1900
1901  LCSQKDFLKECSDRIFRFLAELCRTDRTIDIQVRTDPTPIAFDYQFLHSF  1950
1951  VYLYGLEVRRNVLNEAKHDDGDIDGDDFYKTTFLLIDDVLGQLGQPKMEF  2000
2001  SNEIPIYIREHMDDYPELYEFMNRHAFRNIETSTAYSPSVHESTSSEGIP  2050
2051  IITLTMSNFSDRHVDIDTVAYKFLQIYARIWTTKHCLIIDCTEFDEGGLD  2100
2101  MRKFISLVMGLLPEVAPKNCIGCYYFNVNETFMDNYGKCLDKDNVYVSSK  2150
2151  IPHYFINSNSDEGLMKSVGITGQGLKVLQDIRVSLHDITLYDEKRNRFTP  2200
2201  VSLKIGDIYFQVLHETPRQYKIRDMGTLFDVKFNDVYEISRIFEVHVSSI  2250
2251  TGVAAEFTVTFQDERRLIFSSPKYLEIVKMFYYAQIRLESEYEMDNNSST  2300
2301  SSPNSNNKDKQQKERTKLLCHLLLVSLIGLFDESKKMKNSSYNLIAATEA  2350
2351  SFGLNFGSHFHRSPEVYVPEDTTTFLGVIGKSLAESNPELTAYMFIYVLE  2400
2401  ALKNNVIPHVYIPHTICGLSYWIPNLYQHVYLADDEEGPENISHIFRILI  2450
2451  RLSVRETDFKAVYMQYVWLLLLDDGRLTDIIVDEVINHALERDSENRDWK  2500
2501  KTISLLTVLPTTEVANNIIQKILAKIRSFLPSLKLEAMTQSWSELTILVK  2550
2551  ISIHVFFETSLLVQMYLPEILFIVSLLIDVGPRELRSSLHQLLMNVCHSL  2600
2601  AINSALPQDHRNNLDEISDIFAHQKVKFMFGFSEDKGRILQIFSASSFAS  2650
2651  KFNILDFFINNILLLMEYSSTYEANVWKTRYKKYVLESVFTSNSFLSARS  2700
2701  IMIVGIMGKSYITEGLCKAMLIETMKVIAEPKITDEHLFLAISHIFTYSK  2750
2751  IVEGLDPNLDLMKHLFWFSTLFLESRHPIIFEGALLFVSNCIRRLYMAQF  2800
2801  ENESETSLISTLLKGRKFAHTFLSKIENLSGIVWNEDNFTHILIFIINKG  2850
2851  LSNPFIKSTAFDFLKMMFRNSYFEHQINQKSDHYLCYMFLLYFVLNCNQF  2900
2901  EELLGDVDFEGEMVNIENKNTIPKILLEWLSSDNENANITLYQGAILFKC  2950
2951  SVTDEPSRFRFALIIRHLLTKKPICALRFYSVIRNEIRKISAFEQNSDCV  3000
3001  PLAFDILNLLVTHSESNSLEKLHEESIERLTKRGLSIVTSSGIFAKNSDM  3050
3051  MIPLDVKPEDIYERKRIMTMILSRMSCSA                       3079

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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