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

NucPred

Fetching Q00083 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MEDSQRGNASMMSMMDDPFVVSPEGARDPPSTNQYSTFDAQLFNLDASTP    50
  51  AQAKRALEAHLAETERRLEEASKLGTALVEQRKDLEDKLREVEQQQEEGQ   100
 101  IGEELRRKLADLEREYNEIGQETARAFLAPKRLAGGDDGHLGTPSMDQKS   150
 151  PLHSALFAGQATNSPSKVSVPSRKSRNQSNRVHDIEFATEISTSLLAQVR   200
 201  QLQSLLAEREETLKTVNLEKSRLELEAEGYAQRIRALDESEERYKDENWA   250
 251  LETKIHELMAAVKDVTDRETKLTSSLGAATAEKSAMERELEDLKQANAKL   300
 301  IEDHTAAQKANDAEINTLRRNLSAGDAERLTLQRKLEDMNTQNQELAKAV   350
 351  AMRLRQQEAESTREVVRPHDSEDEEQATPENSPPPSPNKFTPRHNHLETE   400
 401  TLRSSLGHAHRMIQNLRSTIHREKTEKIELKRMLQEARDEVEQRRRDSVA   450
 451  ANGPTNKRQKTKAETRKPARPDLLGAGRKKAEVEIHDSDWESNAGDISPT   500
 501  HKASNDSRDRRGDQPIDDRSDAYHTATEADDPFETANERETTTESEAFQT   550
 551  GVESMAGDSTDSDELTETEDRVQRTPRGRVSSMTLAKARDRTSYYSTAST   600
 601  SADEGDSTDPGTPSISQFSTPRYRLRKKRSVLRKIRPSGEAPMAFNSRPS   650
 651  SARESPSTSFTRDTSAAPEGQSLFAELAEVDGDEDDFGPPMQFEAASPST   700
 701  PRMLPGFDSRRPSAVTVELPSKPDMVDSGVMTDPWEPNLHLASQTDDETV   750
 751  ISVPVTPDKPTMSDASTGMDVVESPSLVHSSTQWTPLKPNAETSDDHVLS   800
 801  VPTPPKMAWDGQTLNEERKVDIPDSPTTQRELNISSVSFEETEPVAPSFP   850
 851  ELRTAFFVGSTTEPVAAPVPVPPEVALSPISSQTTQPTEPVIPAPPEPEP   900
 901  IYVPEMAFSQILVEDTLPILAKLPEPAPERVFAEQGTSTDIAELSVSAIS   950
 951  SEQTEPVEPVYEPKQDVAIVAEAVPEGPLSFVEQGTNTDDVEISFPAISS  1000
1001  VETEPVAPVRETKDDVPEPVLSLTEQGTSTDTVEFSVSSISSEETEPVEP  1050
1051  IREAKEEAAAVDDVASESTHPVLSIFLTPPAYTEPTAPKLQEAVIPPAPQ  1100
1101  LALSTVSSVETPPVQYTPDVLILPTPPALDENTPPSVMASTAKATKSAPP  1150
1151  LVVVDDNTDKGTADGLVTQQNGVTLPLGAISGNAAPRRARSGSSNQADQG  1200
1201  AQTILSSKQIDQLLIDRASVRPLSPPDSDKLNEMSNSPFATPKARSRPVP  1250
1251  QASNASLHKRPGSAASQASSVQIHPPLPADHKEAIMAAEKKSIDQRPASA  1300
1301  GLMGPPLAPASAVRASSQQRPRTPNESALQVGSAKTTTSRASVRRDSHMS  1350
1351  RRSSVSSFASELEERFNMQPNPPFAPQGYSTGTDPRMIQAITQTMIGEFL  1400
1401  WKYTRRAVSGEISNTRHRRYFWVHPYTRTLYWSEHDPQSAGKSEGRTKSV  1450
1451  SIEAVRVVADDNPYPPGLHCKSLEVVSPGRRIRFTATTSQRHETWFNALS  1500
1501  YLLVRNGPEDEEAENGVTLDDIDEFNPGFRSRSRQTARMSVSSSQSRGTR  1550
1551  GLPKQRSGSAMSLRPSVTPGRASPYPPSHYSDQARQASSSRLSTIFNSTI  1600
1601  KGSFGRKGPYAASSLNEDSIHNHDDSVEDLRHMMDRGDDVDRLENVRACC  1650
1651  DGKHDVSSLSRTSRYSPRANRIHSHH                          1676

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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