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

NucPred

Fetching P51112 from www.uniprot.org...

The NucPred score for your sequence is 0.84 (see score help below)

   1  MATMEKLMKAFESLKSFQQQQGPPTAEEIVQRQKKEQATTKKDRVSHCLT    50
  51  ICENIVAQSLRTSPEFQKLLGIAMEMFLLCSDDSESDVRMVADECLNRII   100
 101  KALMDSNLPRLQLELYKEIKKNGASRSLRAALWRFAELAHLIRPQKCRPY   150
 151  LVNLLPCLTRITKRQEETIQETLAAAMPKIMAALGHFANDGEIKMLLKSF   200
 201  VANLKSSSPTIRRTAASSAVSVCQHSRRTSYFYTWLLNVLLGLLVPVDEE   250
 251  HHSHLILGVLLTLRYLMPLLQQQVNTISLKGSFGVMQKEADVQPAPEQLL   300
 301  QVYELTLHYTQHWDHNVVTAALELLQQTLRTPPPELLHVLITAGSIQHAS   350
 351  VFRQDIESRARSGSILELIAGGGSTCSPLLHRKHRGKMLSGEEDALEDDP   400
 401  EKTDVTTGYFTAVGADNSSAAQVDIITQQPRSSQHTIQPGDSVDLSASSE   450
 451  QGGRGGGASASDTPESPNDEEDMLSRSSSCGANITPETVEDATPENPAQE   500
 501  GRPVGGSGAYDHSLPPSDSSQTTTEGPDSAVTPSDVAELVLDGSESQYSG   550
 551  MQIGTLQDEEDEGTATSSQEDPPDPFLRSALALSKPHLFESRGHNRQGSD   600
 601  SSVDRFIPKDEPPEPEPDNKMSRIKGAIGHYTDRGAEPVVHCVRLLSASF   650
 651  LLTGQKNGLTPDRDVRVSVKALAVSCVGAAAALHPEAFFNSLYLEPLDGL   700
 701  RAEEQQYISDVLGFIDHGDPQIRGATAILCAAIIQAALSKMRYNIHSWLA   750
 751  SVQSKTGNPLSLVDLVPLLQKALKDESSVTCKMACSAVRHCIMSLCGSTL   800
 801  SELGLRLVVDLFALKDSSYWLVRTELLETLAEMDFRLVNFLERKSEALHK   850
 851  GEHHYTGRLRLQERVLNDVVIQLLGDDDPRVRHVAASAVSRLVSRLFFDC   900
 901  DQGQADPVVAIARDQSSVYLQLLMHETQPPSQLTVSTITRTYRGFNLSNN   950
 951  VADVTVENNLSRVVTAVSHAFTSSTSRALTFGCCEALCLLAVHFPICTWT  1000
1001  TGWHCGHISSQSSFSSRVGRSRGRTLSVSQSGSTPASSTTSSAVDPERRT  1050
1051  LTVGTANMVLSLLSSAWFPLDLSAHQDALLLCGNLLAAVAPKCLRNPWAG  1100
1101  EDDSSSSSTNTSGGTHKMEEPWAALSDRAFVAMVEQLFSHLLKVLNICAH  1150
1151  VLDDTPPGPPVKATLPSLTNTPSLSPIRRKGKDKDAVDSSSAPLSPKKGN  1200
1201  EANTGRPTESTGSTAVHKSTTLGSFYHLPPYLKLYDVLKATHANFKVMLD  1250
1251  LHSNQEKFGSFLRAALDVLSQLLELATLNDINKCVEEILGYLKSCFSREP  1300
1301  TMATVCVQQLLKTLFGTNLASQYEGFLSGPSRSQGKALRLGSSSLRPGLY  1350
1351  HYCFMAPYTHFTQALADASLRNMVQAEHEQDTSGWFDVMQKTSNQLRSNI  1400
1401  ANAARHRGDKNAIHNHIRLFEPLVIKALKQYTTSTSVALQRQVLDLLAQL  1450
1451  VQLRVNYCLLDSDQVFIGFVLKQFEYIEVGQFRDSEAIIPNIFFFLVLLS  1500
1501  YERYHSKQIISIPKIIQLCDGIMASGRKAVTHAIPALQPIVHDLFVLRGS  1550
1551  NKADAGKELETQKEVVVSMLLRLVQYHQVLEMFILVLQQCHKENEDKWKR  1600
1601  LSRQIADVILPMIAKQQMHLDSPEALGVLNTLFETVAPSSLRPVDMLLKS  1650
1651  MFTTPVTMASVATVQLWVSGILAVLRVLVSQSTEDIVLSRIHELSLSPHL  1700
1701  LSCHTIKRLQQPNLSPSDQPAGDGQQNQEPNGEAQKSLPEETFARFLIQL  1750
1751  VGVLLDDISSRHVKVDITEQQHTFYCQQLGTLLMCLIHVFKSGMFRRITV  1800
1801  AASRLLKGESGSGHSGIEFYPLEGLNSMVHCLITTHPSLVLLWCQVLLII  1850
1851  DYTNYSWWTEVHQTPKGHSLSCTKLLSPHSSGEGEEKPETRLAMINREIV  1900
1901  RRGALILFCDYVCQNLHDSEHLTWLIVNHVRDLIDLSHEPPVQDFISAVH  1950
1951  RNSAASGLFIQAIQSRCDNLNSPTMLKKTLQCLEGIHLSQSGSLLMLYVD  2000
2001  KLLSTPFRVLARMVDTLACRRVEMLLAETLQNSVAQLPLEELHRIQEYLQ  2050
2051  TSGLAQRHQRFYSLLDRFRATVSDTSSPSTPVTSHPLDGDPPPAPELVIA  2100
2101  DKEWYVALVKSQCCLHGDVSLLETTELLTKLPPADLLSVMSCKEFNLSLL  2150
2151  CPCLSLGVQRLLRGQGSLLLETALQVTLEQLAGATGLLPVPHHSFIPTSH  2200
2201  PQSHWKQLAEVYGDPGFYSRVLSLCRALSQYLLTVKQLPSSLRIPSDKEH  2250
2251  LITTFTCAATEVVVWHLLQDQLPLSVDLQWALSCLCLALQQPCVWNKLST  2300
2301  PEYNTHTCSLIYCLHHIILAVAVSPGDQLLHPERKKTKALRHSDDEDQVD  2350
2351  SVHDNHTLEWQACEIMAELVEGLQSVLSLGHHRNTAFPAFLTPTLRNIII  2400
2401  SLSRLPLVNSHTRVPPLVWKLGWSPQPGGEFGTTLPEIPVDFLQEKDVFR  2450
2451  EFLYRINTLGWSNRTQFEETWATLLGVLVTQPITMDQEEETQQEEDLERT  2500
2501  QLNVLAVQAITSLVLSAMTLPTAGNPAVSCLEQQPRNKSLKALETRFGRK  2550
2551  LAVIRGEVEREIQALVSKRDNVHTYHPYHAWDPVPSLSAASPGTLISHEK  2600
2601  LLLQINTERELGNMDYKLGQVSIHSVWLGNNITPLREEEWGEDEDDEADP  2650
2651  PAPTSPPLSPINSRKHRAGVDIHSCSQFLLELYSQWVIPGSPSNRKTPTI  2700
2701  LISEVVRSLLAVSDLFTERNQFDMMFSTLMELQKLHPPEDEILNQYLVPA  2750
2751  ICKAAAVLGMDKAIAEPVCRLLETTLRSTHLPSRMGALHGVLYVLECDLL  2800
2801  DDTAKQLIPTVSEYLLSNLRAIAHCVNLHNQQHVLVMCAVAFYMMENYPL  2850
2851  DVGTEFMAGIIQLCGVMVSASEDSTPSIIYHCVLRGLERLLLSEQLSRVD  2900
2901  GEALVKLSVDRVNMPSPHRAMAALGLMLTCMYTGKEKASPAARSAHSDPQ  2950
2951  VPDSESIIVAMERVSVLFDRIRKGLPSEARVVARILPQFLDDFFPPQDIM  3000
3001  NKVIGEFLSNQQPYPQFMATVVYKVFQTLHATGQSSMVRDWVLLSLSNFT  3050
3051  QRTPVAMAMWSLSCFFVSASTSQWISALLPHVISRMGSSDVVDVNLFCLV  3100
3101  AMDFYRHQIDEELDRRAFQSVFETVASPGSPYFQLLACLQSIHQDKSL    3148

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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