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

NucPred

Fetching Q92766 from www.uniprot.org...

The NucPred score for your sequence is 0.97 (see score help below)

   1  MTSSSPAGLEGSDLSSINTMMSAVMSVGKVTENGGSPQGIKSPSKPPGPN    50
  51  RIGRRNQETKEEKSSYNCPLCEKICTTQHQLTMHIRQHNTDTGGADHSCS   100
 101  ICGKSLSSASSLDRHMLVHSGERPYKCTVCGQSFTTNGNMHRHMKIHEKD   150
 151  PNSATATAPPSPLKRRRLSSKRKLSHDAESEREDPAPAKKMVEDGQSGDL   200
 201  EKKADEVFHCPVCFKEFVCKYGLETHMETHSDNPLRCDICCVTFRTHRGL   250
 251  LRHNALVHKQLPRDAMGRPFIQNNPSIPAGFHDLGFTDFSCRKFPRISQA   300
 301  WCETNLRRCISEQHRFVCDTCDKAFPMLCSLALHKQTHVAADQGQEKPQA   350
 351  TPLPGDALDQKGFLALLGLQHTKDVRPAPAEEPLPDDNQAIQLQTLKCQL   400
 401  PQDPGCTNLLSLSPFEAASLGGSLTVLPATKDSIKHLSLQPFQKGFIIQP   450
 451  DSSIVVKPISGESAIELADIQQILKMAASAPPQISLPPFSKAPAAPLQAI   500
 501  FKHMPPLKPKPLVTPRTVVATSTPPPLINAQQASPGCISPSLPPPPLKLL   550
 551  KGSVEAASNAHLLQSKSGTQPHAATRLSLQQPRAELPGQPEMKTQLEQDS   600
 601  IIEALLPLSMEAKIKQEITEGELKAFMTAPGGKKTPAMRKVLYPCRFCNQ   650
 651  VFAFSGVLRAHVRSHLGISPYQCNICDYIAADKAALIRHLRTHSGERPYI   700
 701  CKICHYPFTVKANCERHLRKKHLKATRKDIEKNIEYVSSSAAELVDAFCA   750
 751  PDTVCRLCGEDLKHYRALRIHMRTHCGRGLGGGHKGRKPFECKECSAAFA   800
 801  AKRNCIHHILKQHLHVPEQDIESYVLAADGLGPAEAPAAEASGRGEDSGC   850
 851  AALGDCKPLTAFLEPQNGFLHRGPTQPPPPHVSIKLEPASSFAVDFNEPL   900
 901  DFSQKGLALVQVKQENISFLSPSSLVPYDCSMEPIDLSIPKNFRKGDKDL   950
 951  ATPSEAKKPEEEAGSSEQPSPCPAPGPSLPVTLGPSGILESPMAPAPAAT  1000
1001  PEPPAQPLQGPVQLAVPIYSSALVSSPPLVGSSALLSGTALLRPLRPKPP  1050
1051  LLLPKPPVTEELPPLASIAQIISSVSSAPTLLKTKVADPGPASTGSNTTA  1100
1101  SDSLGGSVPKAATTATPAATTSPKESSEPPAPASSPEAASPTEQGPAGTS  1150
1151  KKRGRKRGMRSRPRANSGGVDLDSSGEFASIEKMLATTDTNKFSPFLQTA  1200
1201  EDNTQDEVAGAPADHHGPSDEEQGSPPEDKLLRAKRNSYTNCLQKITCPH  1250
1251  CPRVFPWASSLQRHMLTHTDSQSDAETAAAAGEVLDLTSRDREQPSEGAT  1300
1301  ELRQVAGDAPVEQATAETASPVHREEHGRGESHEPEEEHGTEESTGDADG  1350
1351  AEEDASSNQSLDLDFATKLMDFKLAEGDGEAGAGGAASQEQKLACDTCGK  1400
1401  SFKFLGTLSRHRKAHGRQEPKDEKGDGASTAEEGPQPAPEQEEKPPETPA  1450
1451  EVVESAPGAGEAPAEKLAEETEGPSDGESAAEKRSSEKSDDDKKPKTDSP  1500
1501  KSVASKADKRKKVCSVCNKRFWSLQDLTRHMRSHTGERPYKCQTCERTFT  1550
1551  LKHSLVRHQRIHQKARHAKHHGKDSDKEERGEEDSENESTHSGNNAVSEN  1600
1601  EAELAPNASNHMAVTRSRKEGLASATKDCSHREEKVTAGWPSEPGQGDLN  1650
1651  PESPAALGQDLLEPRSKRPAHPILATADGASQLVGME               1687

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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