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

NucPred

Fetching P25391 from www.uniprot.org...

The NucPred score for your sequence is 0.59 (see score help below)

   1  MRGGVLLVLLLCVAAQCRQRGLFPAILNLASNAHISTNATCGEKGPEMFC    50
  51  KLVEHVPGRPVRNPQCRICDGNSANPRERHPISHAIDGTNNWWQSPSIQN   100
 101  GREYHWVTITLDLRQVFQVAYVIIKAANAPRPGNWILERSLDGTTFSPWQ   150
 151  YYAVSDSECLSRYNITPRRGPPTYRADDEVICTSYYSRLVPLEHGEIHTS   200
 201  LINGRPSADDLSPKLLEFTSARYIRLRLQRIRTLNADLMTLSHREPKELD   250
 251  PIVTRRYYYSIKDISVGGMCICYGHASSCPWDETTKKLQCQCEHNTCGES   300
 301  CNRCCPGYHQQPWRPGTVSSGNTCEACNCHNKAKDCYYDESVAKQKKSLN   350
 351  TAGQFRGGGVCINCLQNTMGINCETCIDGYYRPHKVSPYEDEPCRPCNCD   400
 401  PVGSLSSVCIKDDLHSDLHNGKQPGQCPCKEGYTGEKCDRCQLGYKDYPT   450
 451  CVSCGCNPVGSASDEPCTGPCVCKENVEGKACDRCKPGFYNLKEKNPRGC   500
 501  SECFCFGVSDVCSSLSWPVGQVNSMSGWLVTDLISPRKIPSQQDALGGRH   550
 551  QVSINNTAVMQRLAPKYYWAAPEAYLGNKLTAFGGFLKYTVSYDIPVETV   600
 601  DSNLMSHADVIIKGNGLTLSTQAEGLSLQPYEEYLNVVRLVPENFQDFHS   650
 651  KRQIDRDQLMTVLANVTHLLIRANYNSAKMALYRLESVSLDIASSNAIDL   700
 701  VVAADVEHCECPQGYTGTSCESCLSGYYRVDGILFGGICQPCECHGHAAE   750
 751  CNVHGVCIACAHNTTGVHCEQCLPGFYGEPSRGTPGDCQPCACPLTIASN   800
 801  NFSPTCHLNDGDEVVCDWCAPGYSGAWCERCADGYYGNPTVPGESCVPCD   850
 851  CSGNVDPSEAGHCDSVTGECLKCLGNTDGAHCERCADGFYGDAVTAKNCR   900
 901  ACECHVKGSHSAVCHLETGLCDCKPNVTGQQCDQCLHGYYGLDSGHGCRP   950
 951  CNCSVAGSVSDGCTDEGQCHCVPGVAGKRCDRCAHGFYAYQDGSCTPCDC  1000
1001  PHTQNTCDPETGECVCPPHTQGVKCEECEDGHWGYDAEVGCQACNCSLVG  1050
1051  STHHRCDVVTGHCQCKSKFGGRACDQCSLGYRDFPDCVPCDCDLRGTSGD  1100
1101  ACNLEQGLCGCVEETGACPCKENVFGPQCNECREGTFALRADNPLGCSPC  1150
1151  FCSGLSHLCSELEDYVRTPVTLGSDQPLLRVVSQSNLRGTTEGVYYQAPD  1200
1201  FLLDAATVRQHIRAEPFYWRLPQQFQGDQLMAYGGKLKYSVAFYSLDGVG  1250
1251  TSNFEPQVLIKGGRIRKQVIYMDAPAPENGVRQEQEVAMRENFWKYFNSV  1300
1301  SEKPVTREDFMSVLSDIEYILIKASYGQGLQQSRISDISMEVGRKAEKLH  1350
1351  PEEEVASLLENCVCPPGTVGFSCQDCAPGYHRGKLPAGSDRGPRPLVAPC  1400
1401  VPCSCNNHSDTCDPNTGKCLNCGDNTAGDHCDVCTSGYYGKVTGSASDCA  1450
1451  LCACPHSPPASFSPTCVLEGDHDFRCDACLLGYEGKHCERCSSSYYGNPQ  1500
1501  TPGGSCQKCDCNPHGSVHGDCDRTSGQCVCRLGASGLRCDECEPRHILME  1550
1551  TDCVSCDDECVGVLLNDLDEIGDAVLSLNLTGIIPVPYGILSNLENTTKY  1600
1601  LQESLLKENMQKDLGKIKLEGVAEETDNLQKKLTRMLASTQKVNRATERI  1650
1651  FKESQDLAIAIERLQMSITEIMEKTTLNQTLDEDFLLPNSTLQNMQQNGT  1700
1701  SLLEIMQIRDFTQLHQNATLELKAAEDLLSQIQENYQKPLEELEVLKEAA  1750
1751  SHVLSKHNNELKAAEALVREAEAKMQESNHLLLMVNANLREFSDKKLHVQ  1800
1801  EEQNLTSELIVQGRGLIDAAAAQTDAVQDALEHLEDHQDKLLLWSAKIRH  1850
1851  HIDDLVMHMSQRNAVDLVYRAEDHAAEFQRLADVLYSGLENIRNVSLNAT  1900
1901  SAAYVHYNIQSLIEESEELARDAHRTVTETSLLSESLVSNGKAAVQRSSR  1950
1951  FLKEGNNLSRKLPGIALELSELRNKTNRFQENAVEITRQTNESLLILRAI  2000
2001  PKGIRDKGAKTKELATSASQSAVSTLRDVAGLSQELLNTSASLSRVNTTL  2050
2051  RETHQLLQDSTMATLLAGRKVKDVEIQANLLFDRLKPLKMLEENLSRNLS  2100
2101  EIKLLISQARKQAASIKVAVSADRDCIRAYQPQISSTNYNTLTLNVKTQE  2150
2151  PDNLLFYLGSSTASDFLAVEMRRGRVAFLWDLGSGSTRLEFPDFPIDDNR  2200
2201  WHSIHVARFGNIGSLSVKEMSSNQKSPTKTSKSPGTANVLDVNNSTLMFV  2250
2251  GGLGGQIKKSPAVKVTHFKGCLGEAFLNGKSIGLWNYIEREGKCRGCFGS  2300
2301  SQNEDPSFHFDGSGYSVVEKSLPATVTQIIMLFNTFSPNGLLLYLGSYGT  2350
2351  KDFLSIELFRGRVKVMTDLGSGPITLLTDRRYNNGTWYKIAFQRNRKQGV  2400
2401  LAVIDAYNTSNKETKQGETPGASSDLNRLDKDPIYVGGLPRSRVVRRGVT  2450
2451  TKSFVGCIKNLEISRSTFDLLRNSYGVRKGCLLEPIRSVSFLKGGYIELP  2500
2501  PKSLSPESEWLVTFATTNSSGIILAALGGDVEKRGDREEAHVPFFSVMLI  2550
2551  GGNIEVHVNPGDGTGLRKALLHAPTGTCSDGQAHSISLVRNRRIITVQLD  2600
2601  ENNPVEMKLGTLVESRTINVSNLYVGGIPEGEGTSLLTMRRSFHGCIKNL  2650
2651  IFNLELLDFNSAVGHEQVDLDTCWLSERPKLAPDAEDSKLLPEPRAFPEQ  2700
2701  CVVDAALEYVPGAHQFGLTQNSHFILPFNQSAVRKKLSVELSIRTFASSG  2750
2751  LIYYMAHQNQADYAVLQLHGGRLHFMFDLGKGRTKVSHPALLSDGKWHTV  2800
2801  KTDYVKRKGFITVDGRESPMVTVVGDGTMLDVEGLFYLGGLPSQYQARKI  2850
2851  GNITHSIPACIGDVTVNSKQLDKDSPVSAFTVNRCYAVAQEGTYFDGSGY  2900
2901  AALVKEGYKVQSDVNITLEFRTSSQNGVLLGISTAKVDAIGLELVDGKVL  2950
2951  FHVNNGAGRITAAYEPKTATVLCDGKWHTLQANKSKHRITLIVDGNAVGA  3000
3001  ESPHTQSTSVDTNNPIYVGGYPAGVKQKCLRSQTSFRGCLRKLALIKSPQ  3050
3051  VQSFDFSRAFELHGVFLHSCPGTES                           3075

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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