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

NucPred

Fetching P18963 from www.uniprot.org...

The NucPred score for your sequence is 0.83 (see score help below)

   1  MNQSDPQDKKNFPMEYSLTKHLFFDRLLLVLPIESNLKTYADVEADSVFN    50
  51  SCRSIILNIAITKDLNPIIENTLGLIDLIVQDEEITSDNITDDIAHSILV   100
 101  LLRLLSDVFEYYWDQNNDFKKIRNDNYKPGFSSHRPNFHTSRPKHTRINP   150
 151  ALATMLLCKISKLKFNTRTLKVLQNMSHHLSGSATISKSSILPDSQEFLQ   200
 201  KRNYPAYTEKIDLTIDYIQRFISASNHVEFTKCVKTKVVAPLLISHTSTE   250
 251  LGVVNHLDLFGCEYLTDKNLLAYLDILQHLSSYMKRTIFHSLLLYYASKA   300
 301  FLFWIMARPKEYVKIYNNLISSDYNSPSSSSDNGGSNNSDKTSISQLVSL   350
 351  LFDDVYSTFSVSSLLTNVNNDHHYHLHHSSSSSKTTNTNSPNSISKTSIK   400
 401  QSSVNASGNVSPSQFSTGNDASPTSPMASLSSPLNTNILGYPLSPITSTL   450
 451  GQANTSTSTTAATTKTDADTPSTMNTNNNNNNNNSANLNNIPQRIFSLDD   500
 501  ISSFNSSRKSLNLDDSNSLFLWDTSQHSNASMTNTNMHAGVNNSQSQNDQ   550
 551  SSLNYMENIMELYSNYTGSELSSHTAILRFLVVLTLLDSEVYDEMNSNSY   600
 601  RKISEPIMNINPKDSNTSSWGSASKNPSIRHLTHGLKKLTLQQGRKRNVK   650
 651  FLTYLIRNLNGGQFVSDVSLIDSIRSILFLMTMTSSISQIDSNIASVIFS   700
 701  KRFYNLLGQNLEVGTNWNSATANTFISHCVERNPLTHRRLQLEFFASGLQ   750
 751  LDSDLFLRHLQLEKELNHIDLPKISLYTEGFRVFFHLVSTKKLHEDIAEK   800
 801  TSSVLKRLFCIIADILLKATPYFDDNVTKIIASILDGHILDQFDAARTLS   850
 851  NDDHVSFDAATSVYTEPTEIIHNSSDASLVSSLSQSPLSINSGSNITNTR   900
 901  TWDIQSILPTLSNRSSASDLSLSNILTNPLEAQQNNNANLLAHRLSGVPT   950
 951  TKRYASPNDSERSRQSPYSSPPQLQQSDLPSPLSVLSSSAGFSSNHSITA  1000
1001  TPTILKNIKSPKPNKTKKIADDKQLKQPSYSRVILSDNDEARKIMMNIFS  1050
1051  IFKRMTNWFIRPDANTEFPKTFTDIIKPLFVSILDSNQRLQVTARAFIEI  1100
1101  PLSYIATFEDIDNDLDPRVLNDHYLLCTYAVTLFASSLFDLKLENAKREM  1150
1151  LLDIIVKFQRVRSYLSNLAEKHNLVQAIITTERLTLPLLVGAVGSGIFIS  1200
1201  LYCSRGNTPRLIKISCCEFLRSLRFYQKYVGALDQYSIYNIDFIDAMAQD  1250
1251  NFTASGSVALQRRLRNNILTYIKGSDSILLDSMDVIYKKWFYFSCSKSVT  1300
1301  QEELVDFRSLAGILASMSGILSDMQELEKSKSAPDNEGDSLSFESRNPAY  1350
1351  EVHKSLKLELTKKMNFFISKQCQWLNNPNLLTRENSRDILSIELHPLSFN  1400
1401  LLFNNLGLKIDELMSIDLSKSHEDSSFVLLEQIIIIIRTILKRDDDEKIM  1450
1451  LLFSTDLLDAVDKLIEIVEKISIKSSKYYKGIIQMSKMFRAFEHSEKNLG  1500
1501  ISNHFHLKNKWLKLVIGWFKLSINKDYDFENLSRPLREMDLQKRDEDFLY  1550
1551  IDTSIESAKALAYLTHNVPLEIPPSSSKEDWNRSSTVSFGNHFTILLKGL  1600
1601  EKSADLNQFPVSLRHKISILNENVIIALTNLSNANVNVSLKFTLPMGYSP  1650
1651  NKDIRIAFLRVFIDIVTNYPVNPEKHEMDKMLAIDDFLKYIIKNPILAFF  1700
1701  GSLACSPADVDLYAGGFLNAFDTRNASHILVTELLKQEIKRAARSDDILR  1750
1751  RNSCATRALSLYTRSRGNKYLIKTLRPVLQGIVDNKESFEIDKMKPGSEN  1800
1801  SEKMLDLFEKYMTRLIDAITSSIDDFPIELVDICKTIYNAASVNFPEYAY  1850
1851  IAVGSFVFLRFIGPALVSPDSENIIIVTHAHDRKPFITLAKVIQSLANGR  1900
1901  ENIFKKDILVSKEEFLKTCSDKIFNFLSELCKIPTNNFTVNVREDPTPIS  1950
1951  FDYSFLHKFFYLNEFTIRKEIINESKLPGEFSFLKNTVMLNDKILGVLGQ  2000
2001  PSMEIKNEIPPFVVENREKYPSLYEFMSRYAFKKVDMKEEEEDNAPFVHE  2050
2051  AMTLDGIQIIVVTFTNCEYNNFVMDSLVYKVLQIYARMWCSKHYVVIDCT  2100
2101  TFYGGKANFQKLTTLFFSLIPEQASSNCMGCYYFNVNKSFMDQWASSYTV  2150
2151  ENPYLVTTIPRCFINSNTDQSLIKSLGLSGRSLEVLKDVRVTLHDITLYD  2200
2201  KEKKKFCPVSLKIGNKYFQVLHEIPQLYKVTVSNRTFSIKFNNVYKISNL  2250
2251  ISVDVSNTTGVSSEFTLSLDNEEKLVFCSPKYLEIVKMFYYAQLKMEEDF  2300
2301  GTDFSNDISFSTSSSAVNASYCNVKEVGEIISHLSLVILVGLFNEDDLVK  2350
2351  NISYNLLVATQEAFNLDFGTRLHKSPETYVPDDTTTFLALIFKAFSESST  2400
2401  ELTPYIWKYMLDGLENDVIPQEHIPTVVCSLSYWVPNLYEHVYLANDEEG  2450
2451  PEAISRIIYSLIRLTVKEPNFTTAYLQQIWFLLALDGRLTNVIVEEIVSH  2500
2501  ALDRDSENRDWMKAVSILTSFPTTEIACQVIEKLINMIKSFLPSLAVEAS  2550
2551  AHSWSELTILSKISVSIFFESPLLSQMYLPEILFAVSLLIDVGPSEIRVS  2600
2601  LYELLMNVCHSLTNNESLPERNRKNLDIVCATFARQKLNFISGFSQEKGR  2650
2651  VLPNFAASSFSSKFGTLDLFTKNIMLLMEYGSISEGAQWEAKYKKYLMDA  2700
2701  IFGHRSFFSARAMMILGIMSKSHTSLFLCKELLVETMKVFAEPVVDDEQM  2750
2751  FIIIAHVFTYSKIVEGLDPSSELMKELFWLATICVESPHPLLFEGGLLFM  2800
2801  VNCLKRLYTVHLQLGFDGKSLAKKLMESRNFAATLLAKLESYNGCIWNED  2850
2851  NFPHIILGFIANGLSIPVVKGAALDCLQALFKNTYYERKSNPKSSDYLCY  2900
2901  LFLLHLVLSPEQLSTLLLEVGFEDELVPLNNTLKVPLTLINWLSSDSDKS  2950
2951  NIVLYQGALLFSCVMSDEPCKFRFALLMRYLLKVNPICVFRFYTLTRKEF  3000
3001  RRLSTLEQSSEAVAVSFELIGMLVTHSEFNYLEEFNDEMVELLKKRGLSV  3050
3051  VKPLDIFDQEHIEKLKGEGEHQVAIYERKRLATMILARMSCS          3092

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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