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

NucPred

Fetching Q8IZF6 from www.uniprot.org...

The NucPred score for your sequence is 0.56 (see score help below)

   1  MKEHIIYQKLYGLILMSSFIFLSDTLSLKGKKLDFFGRGDTYVSLIDTIP    50
  51  ELSRFTACIDLVFMDDNSRYWMAFSYITNNALLGREDIDLGLAGDHQQLI   100
 101  LYRLGKTFSIRHHLASFQWHTICLIWDGVKGKLELFLNKERILEVTDQPH   150
 151  NLTPHGTLFLGHFLKNESSEVKSMMRSFPGSLYYFQLWDHILENEEFMKC   200
 201  LDGNIVSWEEDVWLVNKIIPTVDRTLRCFVPENMTIQEKSTTVSQQIDMT   250
 251  TPSQITGVKPQNTAHSSTLLSQSIPIFATDYTTISYSNTTSPPLETMTAQ   300
 301  KILKTLVDETATFAVDVLSTSSAISLPTQSISIDNTTNSMKKTKSPSSES   350
 351  TKTTKMVEAMATEIFQPPTPSNFLSTSRFTKNSVVSTTSAIKSQSAVTKT   400
 401  TSLFSTIESTSMSTTPCLKQKSTNTGALPISTAGQEFIESTAAGTVPWFT   450
 451  VEKTSPASTHVGTASSFPPEPVLISTAAPVDSVFPRNQTAFPLATTDMKI   500
 501  AFTVHSLTLPTRLIETTPAPRTAETELTSTNFQDVSLPRVEDAMSTSMSK   550
 551  ETSSKTFSFLTSFSFTGTESVQTVIDAEATRTALTPEITLASTVAETMLS   600
 601  STITGRVYTQNTPTADGHLLTLMSTRSASTSKAPESGPTSTTDEAAHLFS   650
 651  SNETIWTSRPDQALLASMNTTTILTFVPNENFTSAFHENTTYTEYLSATT   700
 701  NITPLKASPEGKGTTANDATTARYTTAVSKLTSPWFANFSIVSGTTSITN   750
 751  MPEFKLTTLLLKTIPMSTKPANELPLTPRETVVPSVDIISTLACIQPNFS   800
 801  TEESASETTQTEINGAIVFGGTTTPVPKSATTQRLNATVTRKEATSHYLM   850
 851  RKSTIAAVAEVSPFSTMLEVTDESAQRVTASVTVSSFPDIEKLSTPLDNK   900
 901  TATTEVRESWLLTKLVKTTPRSSYNEMTEMFNFNHTYVAHWTSETSEGIS   950
 951  AGSPTSGSTHIFGEPLGASTTRISETSFSTTPTDRTATSLSDGILPPQPT  1000
1001  AAHSSATPVPVTHMFSLPVNGSSVVAEETEVTMSEPSTLARAFSTSVLSD  1050
1051  VSNLSSTTMTTALVPPLDQTASTTIVIVPTHGDLIRTTSEATVISVRKTS  1100
1101  MAVPSLTETPFHSLRLSTPVTAKAETTLFSTSVDTVTPSTHTLVCSKPPP  1150
1151  DNIPPASSTHVISTTSTPEATQPISQVEETSTYALSFPYTFSGGGVVASL  1200
1201  ATGTTETSVVDETTPSHISANKLTTSVNSHISSSATYRVHTPVSIQLVTS  1250
1251  TSVLSSDKDQMTISLGKTPRTMEVTEMSPSKNSFISYSRGTPSLEMTDTG  1300
1301  FPETTKISSHQTHSPSEIPLGTPSDGNLASSPTSGSTQITPTLTSSNTVG  1350
1351  VHIPEMSTSLGKTALPSQALTITTFLCPEKESTSALPAYTPRTVEMIVNS  1400
1401  TYVTHSVSYGQDTSFVDTTTSSSTRISNPMDINTTFSHLHSLRTQPEVTS  1450
1451  VASFISESTQTFPESLSLSTAGLYNDGFTVLSDRITTAFSVPNVPTMLPR  1500
1501  ESSMATSTPIYQMSSLPVNVTAFTSKKVSDTPPIVITKSSKTMHPGCLKS  1550
1551  PCTATSGPMSEMSSIPVNNSAFTPATVSSDTSTRVGLFSTLLSSVTPRTT  1600
1601  MTMQTSTLDVTPVIYAGATSKNKMVSSAFTTEMIEAPSRITPTTFLSPTE  1650
1651  PTLPFVKTVPTTIMAGIVTPFVGTTAFSPLSSKSTGAISSIPKTTFSPFL  1700
1701  SATQQSSQADEATTLGILSGITNRSLSTVNSGTGVALTDTYSRITVPENM  1750
1751  LSPTHADSLHTSFNIQVSPSLTSFKSASGPTKNVKTTTNCFSSNTRKMTS  1800
1801  LLEKTSLTNYATSLNTPVSYPPWTPSSATLPSLTSFVYSPHSTEAEISTP  1850
1851  KTSPPPTSQMVEFPVLGTRMTSSNTQPLLMTSWNIPTAEGSQFPISTTIN  1900
1901  VPTSNEMETETLHLVPGPLSTFTASQTGLVSKDVMAMSSIPMSGILPNHG  1950
1951  LSENPSLSTSLRAITSTLADVKHTFEKMTTSVTPGTTLPSILSGATSGSV  2000
2001  ISKSPILTWLLSSLPSGSPPATVSNAPHVMTSSTVEVSKSTFLTSDMISA  2050
2051  HPFTNLTTLPSATMSTILTRTIPTPTLGGITTGFPTSLPMSINVTDDIVY  2100
2101  ISTHPEASSRTTITANPRTVSHPSSFSRKTMSPSTTDHTLSVGAMPLPSS  2150
2151  TITSSWNRIPTASSPSTLIIPKPTLDSLLNIMTTTSTVPGASFPLISTGV  2200
2201  TYPFTATVSSPISSFFETTWLDSTPSFLSTEASTSPTATKSTVSFYNVEM  2250
2251  SFSVFVEEPRIPITSVINEFTENSLNSIFQNSEFSLATLETQIKSRDISE  2300
2301  EEMVMDRAILEQREGQEMATISYVPYSCVCQVIIKASSSLASSELMRKIK  2350
2351  SKIHGNFTHGNFTQDQLTLLVNCEHVAVKKLEPGNCKADETASKYKGTYK  2400
2401  WLLTNPTETAQTRCIKNEDGNATRFCSISINTGKSQWEKPKFKQCKLLQE  2450
2451  LPDKIVDLANITISDENAEDVAEHILNLINESPALGKEETKIIVSKISDI  2500
2501  SQCDEISMNLTHVMLQIINVVLEKQNNSASDLHEISNEILRIIERTGHKM  2550
2551  EFSGQIANLTVAGLALAVLRGDHTFDGMAFSIHSYEEGTDPEIFLGNVPV  2600
2601  GGILASIYLPKSLTERIPLSNLQTILFNFFGQTSLFKTKNVTKALTTYVV  2650
2651  SASISDDMFIQNLADPVVITLQHIGGNQNYGQVHCAFWDFENNNGLGGWN  2700
2701  SSGCKVKETNVNYTICQCDHLTHFGVLMDLSRSTVDSVNEQILALITYTG  2750
2751  CGISSIFLGVAVVTYIAFHKLRKDYPAKILINLCTALLMLNLVFLINSWL  2800
2801  SSFQKVGVCITAAVALHYFLLVSFTWMGLEAVHMYLALVKVFNIYIPNYI  2850
2851  LKFCLVGWGIPAIMVAITVSVKKDLYGTLSPTTPFCWIKDDSIFYISVVA  2900
2901  YFCLIFLMNLSMFCTVLVQLNSVKSQIQKTRRKMILHDLKGTMSLTFLLG  2950
2951  LTWGFAFFAWGPMRNFFLYLFAIFNTLQGFFIFVFHCVMKESVREQWQIH  3000
3001  LCCGWLRLDNSSDGSSRCQIKVGYKQEGLKKIFEHKLLTPSLKSTATSST  3050
3051  FKSLGSAQGTPSEISFPNDDFDKDPYCSSP                      3080

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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