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

NucPred

Fetching P87319 from www.uniprot.org...

The NucPred score for your sequence is 0.80 (see score help below)

   1  MLEGLVAGLLNKILGSYVDNLDTKQLNIGVWGGHVSLHNLRIKPEALDKL    50
  51  GIPIEITSGLIGTFTLEIPWSNLRNKSLTINIEDIYLSIHPQAKNSLTRD   100
 101  ELEQSQQALKQEQLDSFEILRKNFRETLEESSSNPNISRKQSFIEYLIAK   150
 151  LTDNIQIYIERIHLRFEDNLSDLEKPYSLGLTLYSLRVTSTDASFTEYLL   200
 201  STDPIPSSCIHKIITVDYFSIYWISKCEISKCTTTEDIFSYLKNLIPSAE   250
 251  KSPAYNYILKPLRATAHVVLFRHPTDQIMQLRGKLSVEEISITLSDHMYY   300
 301  SLLGVIDYFRVVMKQQYYLQYRPKSTPKEKPLEWFKYAILVVKDSVHESR   350
 351  YHWTWKYFKRRRDDRIAYMHIIRKRYLNEQISKEEIDLQKKIEKRNSTYD   400
 401  LIKYRSRVHTSLIEERNSIYLKPKTSAAHGLYDWFSGYIRKPQSQDEDTL   450
 451  ASTDKTAADLTDQEQKEFFSAIEWSGQLYPDTVNLDPDMCMANVEVSIAK   500
 501  GSFVIQSHINGRVIPLIKQRFESFATECFIRPQSLKLKVSLKDLDMFDGI   550
 551  TNPELEPARVIFAKPSVEESESLQKIPEAYRTHLFFLLLDTKPVYKASST   600
 601  LIVHLRTLVIIYNRVCIESLLAFFVPPRTKIEHVSEWGYSAAAKVMTLAR   650
 651  QTRASLDYALEMHKTSDMTIDLQAPLIVVREECTDLKSPTLFLDVGRALV   700
 701  HTQLVDDAIIDKFRKLQSKKINNEQLKQLENLMYDKFTISLFNVRCLIGP   750
 751  DYETGWRCLPKGCDYHILKECSLDINFEISILQKATNLTKFKVSSHMKHA   800
 801  EIMFSDVQYKVFINMMSNILPTLPVAEIPFTYQQFLDAVKPPPFFDAPDN   850
 851  FQITHTSLGSHANENTAAQFMAQQIFAFYFKVDYAICSLYRRSENYLIPV   900
 901  VRAFTEFYIDLVVRKFDYLVTSKLNDLVIKEFTYPSSLCDNVLVRSSPSP   950
 951  KNNFDDTVFISYTSIDYDSPELDSVYEGVRTTIAVVLSDLILNVEPTGFS  1000
1001  FVYDFIRATFTSLNDEYMIGEDPELTRKISPVEGIIPEDANVRFDNVDIF  1050
1051  LYDCDQHFSTVCLYSANMHMEFREKFFLQARFYDLEVKNHMKSNNPPKTI  1100
1101  VKIDDNDLFIFKYESYDIPKDISKPTCDCVYDISFGSLTFYFQKSYFNAI  1150
1151  YDFLLKLKRFQELFSSIRYAIYYKLYGNKVSLTYPKFELRIKHPKVYFDD  1200
1201  VLDEERNCRMQLIVKPQSFYAFSKCPIVEKNSKKSIFSCEITKVEFHTAV  1250
1251  PSSSHHDVLMEENNVHLDLTYDANYTTGAYVFKATGDLDPVILNMCQSHH  1300
1301  VIFWDLIDVATTFARVDSSFYTSENLRRELDKAFDRSGTAAKLKHPKKTV  1350
1351  VETLDILTTFNLPEIRLNVHTDDFWIHGGDLTQLHSILSFFGFSLDYNFY  1400
1401  SSGRCYAEFSIDSIQLKDCNPQDNVVFLDVLEYSENHNRLVNGCLEYDSQ  1450
1451  NPRYNLVLDIDSPKIFVNLNYLYSIWSIFVHWHRAYYSHLDYLTEVEYFI  1500
1501  MGNPNQNACGEESYWYYRITFVDMTLLFFRNVSDANLYSLPMFFGELLIT  1550
1551  QQSIFAVTANNMKINACPLSETANISNQLADPFGFRYTYSQHTVNKIQII  1600
1601  TNITLDFDSFVLRTTVNDFLFLQTILRKIYNFYYALYDVPTTDVELLKRT  1650
1651  KDDQLATNPDFLQLSVDTGQPSSVFGIRICKEEFLLTVDGIRLLVISQLH  1700
1701  DLPLLNINIKPFQVDLNDWSSELNSNAHLELFMNFYNFSNSHWEPFLEPW  1750
1751  KVGVHISRNPNTSKTAVHVFSREKLDLVITPQLIETLHFGFTKVISTPFP  1800
1801  IEFKCDAPYRIHNYTGHAVSVWADFENAADSCVRHLENNEETDWKFEEWR  1850
1851  QMQDVVKQDQDRSYIGFHFENSKWESLRHVRVNRVGEHIYPLISYDQDEL  1900
1901  KHYMVVDVNLGEDYIKHITLRSPLLLINETQMEIDVVFCDSDGIQRSQIY  1950
1951  HMSPEESCSLPIETAYYYSIHIRPVSEFKFNWTSEAISWKDLVDNKQSLV  2000
2001  TCQHSDNTFSTPACRFAANAELKSQTISNHYPFMHITISALLEVKNLLPI  2050
2051  DLNIRIIDKDQEGVWMSNVGIGECAYVHSINISHVLLLQAESSESHYLPS  2100
2101  SLATIITNDSAQERDEYMTITLQGGRKTRLGLSYTEKYPGIYHIEIFSPY  2150
2151  IIINKSGSFLFVGPKNDYNRISFSSASLSSGEDGKVVPCMFSYSHNYGSR  2200
2201  RCRLRADNSNWSEPVSFDAIGSVFEVELPSKEDHNKVYRLGIFVETGPDG  2250
2251  YSKTNIVTITSRFIVRNKTRWSLVIAEPYNDFIAEIAPEGEEFLTYLRKH  2300
2301  SHPMLKLSSSDCYLWSSSFYIEEIGSTHVRLMTSEGEKLLRLEIVIKNAT  2350
2351  IFISIFEETGDWPYYIKNESGVLLKFWQVNPIDASEGKNNTALLKYHDIP  2400
2401  PHSEVKYSWDYPCCANKEIALCYGDQKCLTTLAEIGPLSPFKFTDASNNT  2450
2451  KFISRDIVANGLSKILILKDYDPSKAVRKPKIYSKVSTEERDFNLEQFDS  2500
2501  GIDLSVKFLLEGIGISLVERNTQELAYLTFHGINLFFTDSHLIRTFKLDV  2550
2551  RWIQIDNQLYGGIYPIILYPSILSQEDTMNDNSLLPTFHSMVAVVKNDTY  2600
2601  GVTYVKYATILLQELTIEIDEDFAFAALEYIKDSVPRSKRNTGKMFDDSL  2650
2651  ELVPENLGNDLKVYFEVLNLQPTEMHLSFVRTERINNTDGTVVSSHNPFV  2700
2701  FFVNVLSMAIGNINDAPVRLNALLMDNAHVSLRRLFELVKNHYSQELLSQ  2750
2751  VHKIVGSADFLGNPVGLFTTITSGFADIFYEPFHGFILNEGSYELGIGFA  2800
2801  KGTASFIKKAVFGITDSISKVTGTISRSLSVITLDPKFQSRRRAARIRNR  2850
2851  PVHILYGVTAGAASLYTGVRSGVRGLALQPIIGARRNGLPGLVKGLGKGL  2900
2901  VGFTTKPLVGLFDFASSISEGARNTTTVFDERHIEKLRLSRLMSDDGVVY  2950
2951  PFQLREALGQYWLKHLDNGRYFKDFYKAHIIIENKVLVILTNNRILFVQP  3000
3001  QQLNCKKEIHLSKVKTVKLQSKEHIFLQLLKGVNFEFSVPENSVRTFFYR  3050
3051  KIRDELAAYKHKVNYELEVAL                               3071

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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