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

NucPred

Fetching Q696W0 from www.uniprot.org...

The NucPred score for your sequence is 0.98 (see score help below)

   1  MRKVTEEKRHSSSMNSSTVETSFIAAPPVFLRKLKWAAVAAGCDVRLRVC    50
  51  VGGNPRPTLHWYHNDDPLVIDHEDYDGLWIRDCQQADGGLYTCVAVNHLG   100
 101  EARTSAVLAVLDLEEDSNSTEDESAEPHVSMEMKEQFMPPQGEAINSQPT   150
 151  GRGRAMLSHIPSDGLVVEREMRALGSRAPGLQEPLSPGRGQLDFKTSEAT   200
 201  PFVQTQPPHKAQASITKSDVDATIDSTATKIKGTKTAMNGAEVSIKSSKI   250
 251  TGSHQSGGLQDPSSIQTPKVSQASSKILDRVRAFEEQSHNSNMPKVSSRL   300
 301  SWGFNRTSSCNSEDETCKAGKFQANTKSDVALKRSFFKQKASSLEEQSTY   350
 351  VQKNFQSKLSEELHRIKKLVGKSNIKKAFSMEQLTQTDKQSSVSTESVPT   400
 401  QVIQKSEETGKHFTNLKAVPDAKERWTTLPKEQSSRLPKINLADKTKQPE   450
 451  NETPPEMNENQENNSKPTQLLDGQVLNEKVSFIPGQCSPMLPRTNVSRKW   500
 501  PKSPAQPMVKDGLVQAPQKPPRLLESISTPPTPFKMTIPTIVVENKPVDE   550
 551  ELDQKEGQIMRQNRDALDDFHTSVEKSIAEAPMSELPRKDALGTAGSELL   600
 601  QCIIKENTVARAPAESLLIITRPMQDVKVKAGETALLECFIAGSQAVDVD   650
 651  WLANGKLIQPALFDCKMQFDGHRCRLLFKSAHENDSGCYTCKLSTAKEEL   700
 701  ICTANLLVIPSKEPLFTRKLEVLEAIEGRSAQFDCKVSGCPPPEVTWTHC   750
 751  EKPLVESDNIHILNVNGHHSLLITHVNKESEGLYTAIAQNVHGKAASSAE   800
 801  LYVQEPRPAISTHMSRLEKMPSIPEEPEVPEGEVERRTMPDFIKPLSDLE   850
 851  VIEGKEAVLKCRVTGLPYPKITWYHNGKKIESTNDRKMTQYRDVHSLVIQ   900
 901  SVCHDHSGVYKCVISNKVGKAACYAHLYVAVSLPEPPDGPPVIESVTGRM   950
 951  ILLSWKKPKNLDPSIDPASLMYVVQQQVLGSTQWTTIASSLTDTSYTVTS  1000
1001  LSKGVCYSFRVLSTTGKTLSKPSQPTDLVQLVDRGEYFRKAPVIIDKPDI  1050
1051  VYAVENQPVTITITINHVQATCTWKRRGVVLVNKLGALEMTTPDDDQHAL  1100
1101  HIAKVKSTDVGQLIFMANNQYGSDLGTLQLVIAVPPIFETIMEDLDVCVG  1150
1151  ETCHFAVVVDGKPDPDILWYKDGVLLAESSHLTFVYDDRECSLVVLNAQP  1200
1201  EDVGVYTCTAKNLAGSVSCKAELTVHTAQNVEEEEEQMEDEATILRRMRM  1250
1251  LTDYYDIHKEIGRGAFSYVKRVKHKNDQSFAAKFISVRAKKKTCALRELA  1300
1301  LLAELDHKSIVRFHDAFEKRRVVIILTELCHEELLERITKRTTILESEVQ  1350
1351  SIIRQLLEGIEYLHQNDIIHLDLKPENILMADQKTDQIRICDFGNALKVK  1400
1401  PNEELYCKYGIPEFIAPEIVNQSPISKSTDIWPVGVITYLCLTGVSPFAG  1450
1451  ENDRDTLLNIRNYNVAFEESMFKDLCREAKGFIIKVLVSNKLRPDATECL  1500
1501  LHPWFKSLTKGKSINTTLHKQVLARRKWQCSLIRYGSKMVMRSISELLDD  1550
1551  SSSHVSLAVPRNLKDGSPPPSSSSDSDEDIDELPFIPMPHTMMFSGSRMS  1600
1601  LTEIHEVDDKVIRGSNESYKKNLNQLDDIPESQIIAGQKNEDYLKNPKRT  1650
1651  DNCLQRGSSVEVDQVASKTRRGLMRRGSSADSALLLQITPEDNEIKDTTE  1700
1701  DSQKHMKKAVSMELPHRSSSPKTAKLSKEDYALKLDLMRQRLLKGGTVNK  1750
1751  NMSGLRGPLLETLGVDDERRTSSLDRNFRNARLNASGDSGTFNNDSSEET  1800
1801  YQKPAFRKRSSLRDENSESISLHRRSGAPLEIPSSSTGDHNVLKIKSTIL  1850
1851  DENKANLPPLFPRDISSKPPTPVLENKQVSKEEANSDVLIMNSSRSAFNL  1900
1901  EDTEIKVEEMKEQDSVPENMNKSTEFPLDILPHDISSNYCSKLQANGKKA  1950
1951  SFLTPLPTPVLKISQPNIQPTAGRPGVFASAFSAHQPNLRSDIKNIDSEE  2000
2001  IFEARFKKRESSLAHGLKRLTRTKSEESSPVPQRKSDEVVYRPGPVGSPL  2050
2051  EFGSTGLKEKSKSVQDLREVDKEVGLGLIGRFSMRARKLQPIDKKEKKEI  2100
2101  SDSVTNKRQLTWATRRSKSLDKKENFETNKENLEKDTKKIAESPVLAVRR  2150
2151  KFESNVSGIFDRVHSRSKDRKDKETKPHIDAEAPNVEKQDMKKINDSPVL  2200
2201  ALRKKFETKVSGISYRKQSQSEGEGTKFEGQKTPLFSRHHRSQSDGLIHK  2250
2251  KMDIPENQLPLQTTTISSKETLNSSSSAHSIESSQTPETEIRSRWDRWGL  2300
2301  SRGKRDRTPSNSRAPATPKEDFPPVFHIALKDHVLLEGNPVTLSCLPAGS  2350
2351  PEPKILWLKDKKPLKLCDGMNLEACPDGRQLLMIMNISKKDAGIYECVAT  2400
2401  NNLASVTTSCILTLACIPNCPGTPEIRQIYNNTVLVQWKPSDTKVPCTYT  2450
2451  IEKKFDGDDKWLTEATGVTDCFFNSSELPSGSTIRFRVACVNKAGQSPYS  2500
2501  NESDGVSIDTKVTPQHQPAKMKTHSPASFPAMTAAVATSAFSLSLPSVFS  2550
2551  QSISPTPAQSADVSNTFLEVQSSPKMSTPLDLPKPASSVNTMPPITQTQT  2600
2601  VSPRSYTAPPSIGRSISPVPTYVPATCSLAPTPVSPSVIVVSSISPIGEG  2650
2651  ASSPTPETPTGQAVSSTKSETTLRQGVPQKPYSFLDEKARGRFGVIRDCR  2700
2701  ENATGKMFIAKIIPYDQQTKQTIIKEYEILKSLRCERIMALHEAYITPRY  2750
2751  LVLITEYCSGKEILQNLIDRFCYSEDDVVGFIVQILQGLEYLHNCKILHL  2800
2801  DIKPDNIMVTNLNVIKIIDFGSAQRFNPLSLQQCSRYLGTLEYMAPEMLK  2850
2851  GDLVGPPADIWSLGVLSYIMLSGRHPFEDKDPQLTEAKIHEAKFDSTKLY  2900
2901  PKVSQSASTFLKKILNSYPWCRPTIKDCLNHSWLHDSYLKKLRRQTLTFT  2950
2951  TTRLKEFMGEHQRRCAESATKHKVILRVYQGGPSSPASPTKYTTQ       2995

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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