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

NucPred

Fetching P49454 from www.uniprot.org...

The NucPred score for your sequence is 0.92 (see score help below)

   1  MSWALEEWKEGLPTRALQKIQELEGQLDKLKKEKQQRQFQLDSLEAALQK    50
  51  QKQKVENEKTEGTNLKRENQRLMEICESLEKTKQKISHELQVKESQVNFQ   100
 101  EGQLNSGKKQIEKLEQELKRCKSELERSQQAAQSADVSLNPCNTPQKIFT   150
 151  TPLTPSQYYSGSKYEDLKEKYNKEVEERKRLEAEVKALQAKKASQTLPQA   200
 201  TMNHRDIARHQASSSVFSWQQEKTPSHLSSNSQRTPIRRDFSASYFSGEQ   250
 251  EVTPSRSTLQIGKRDANSSFFDNSSSPHLLDQLKAQNQELRNKINELELR   300
 301  LQGHEKEMKGQVNKFQELQLQLEKAKVELIEKEKVLNKCRDELVRTTAQY   350
 351  DQASTKYTALEQKLKKLTEDLSCQRQNAESARCSLEQKIKEKEKEFQEEL   400
 401  SRQQRSFQTLDQECIQMKARLTQELQQAKNMHNVLQAELDKLTSVKQQLE   450
 451  NNLEEFKQKLCRAEQAFQASQIKENELRRSMEEMKKENNLLKSHSEQKAR   500
 501  EVCHLEAELKNIKQCLNQSQNFAEEMKAKNTSQETMLRDLQEKINQQENS   550
 551  LTLEKLKLAVADLEKQRDCSQDLLKKREHHIEQLNDKLSKTEKESKALLS   600
 601  ALELKKKEYEELKEEKTLFSCWKSENEKLLTQMESEKENLQSKINHLETC   650
 651  LKTQQIKSHEYNERVRTLEMDRENLSVEIRNLHNVLDSKSVEVETQKLAY   700
 701  MELQQKAEFSDQKHQKEIENMCLKTSQLTGQVEDLEHKLQLLSNEIMDKD   750
 751  RCYQDLHAEYESLRDLLKSKDASLVTNEDHQRSLLAFDQQPAMHHSFANI   800
 801  IGEQGSMPSERSECRLEADQSPKNSAILQNRVDSLEFSLESQKQMNSDLQ   850
 851  KQCEELVQIKGEIEENLMKAEQMHQSFVAETSQRISKLQEDTSAHQNVVA   900
 901  ETLSALENKEKELQLLNDKVETEQAEIQELKKSNHLLEDSLKELQLLSET   950
 951  LSLEKKEMSSIISLNKREIEELTQENGTLKEINASLNQEKMNLIQKSESF  1000
1001  ANYIDEREKSISELSDQYKQEKLILLQRCEETGNAYEDLSQKYKAAQEKN  1050
1051  SKLECLLNECTSLCENRKNELEQLKEAFAKEHQEFLTKLAFAEERNQNLM  1100
1101  LELETVQQALRSEMTDNQNNSKSEAGGLKQEIMTLKEEQNKMQKEVNDLL  1150
1151  QENEQLMKVMKTKHECQNLESEPIRNSVKERESERNQCNFKPQMDLEVKE  1200
1201  ISLDSYNAQLVQLEAMLRNKELKLQESEKEKECLQHELQTIRGDLETSNL  1250
1251  QDMQSQEISGLKDCEIDAEEKYISGPHELSTSQNDNAHLQCSLQTTMNKL  1300
1301  NELEKICEILQAEKYELVTELNDSRSECITATRKMAEEVGKLLNEVKILN  1350
1351  DDSGLLHGELVEDIPGGEFGEQPNEQHPVSLAPLDESNSYEHLTLSDKEV  1400
1401  QMHFAELQEKFLSLQSEHKILHDQHCQMSSKMSELQTYVDSLKAENLVLS  1450
1451  TNLRNFQGDLVKEMQLGLEEGLVPSLSSSCVPDSSSLSSLGDSSFYRALL  1500
1501  EQTGDMSLLSNLEGAVSANQCSVDEVFCSSLQEENLTRKETPSAPAKGVE  1550
1551  ELESLCEVYRQSLEKLEEKMESQGIMKNKEIQELEQLLSSERQELDCLRK  1600
1601  QYLSENEQWQQKLTSVTLEMESKLAAEKKQTEQLSLELEVARLQLQGLDL  1650
1651  SSRSLLGIDTEDAIQGRNESCDISKEHTSETTERTPKHDVHQICDKDAQQ  1700
1701  DLNLDIEKITETGAVKPTGECSGEQSPDTNYEPPGEDKTQGSSECISELS  1750
1751  FSGPNALVPMDFLGNQEDIHNLQLRVKETSNENLRLLHVIEDRDRKVESL  1800
1801  LNEMKELDSKLHLQEVQLMTKIEACIELEKIVGELKKENSDLSEKLEYFS  1850
1851  CDHQELLQRVETSEGLNSDLEMHADKSSREDIGDNVAKVNDSWKERFLDV  1900
1901  ENELSRIRSEKASIEHEALYLEADLEVVQTEKLCLEKDNENKQKVIVCLE  1950
1951  EELSVVTSERNQLRGELDTMSKKTTALDQLSEKMKEKTQELESHQSECLH  2000
2001  CIQVAEAEVKEKTELLQTLSSDVSELLKDKTHLQEKLQSLEKDSQALSLT  2050
2051  KCELENQIAQLNKEKELLVKESESLQARLSESDYEKLNVSKALEAALVEK  2100
2101  GEFALRLSSTQEEVHQLRRGIEKLRVRIEADEKKQLHIAEKLKEREREND  2150
2151  SLKDKVENLERELQMSEENQELVILDAENSKAEVETLKTQIEEMARSLKV  2200
2201  FELDLVTLRSEKENLTKQIQEKQGQLSELDKLLSSFKSLLEEKEQAEIQI  2250
2251  KEESKTAVEMLQNQLKELNEAVAALCGDQEIMKATEQSLDPPIEEEHQLR  2300
2301  NSIEKLRARLEADEKKQLCVLQQLKESEHHADLLKGRVENLERELEIART  2350
2351  NQEHAALEAENSKGEVETLKAKIEGMTQSLRGLELDVVTIRSEKENLTNE  2400
2401  LQKEQERISELEIINSSFENILQEKEQEKVQMKEKSSTAMEMLQTQLKEL  2450
2451  NERVAALHNDQEACKAKEQNLSSQVECLELEKAQLLQGLDEAKNNYIVLQ  2500
2501  SSVNGLIQEVEDGKQKLEKKDEEISRLKNQIQDQEQLVSKLSQVEGEHQL  2550
2551  WKEQNLELRNLTVELEQKIQVLQSKNASLQDTLEVLQSSYKNLENELELT  2600
2601  KMDKMSFVEKVNKMTAKETELQREMHEMAQKTAELQEELSGEKNRLAGEL  2650
2651  QLLLEEIKSSKDQLKELTLENSELKKSLDCMHKDQVEKEGKVREEIAEYQ  2700
2701  LRLHEAEKKHQALLLDTNKQYEVEIQTYREKLTSKEECLSSQKLEIDLLK  2750
2751  SSKEELNNSLKATTQILEELKKTKMDNLKYVNQLKKENERAQGKMKLLIK  2800
2801  SCKQLEEEKEILQKELSQLQAAQEKQKTGTVMDTKVDELTTEIKELKETL  2850
2851  EEKTKEADEYLDKYCSLLISHEKLEKAKEMLETQVAHLCSQQSKQDSRGS  2900
2901  PLLGPVVPGPSPIPSVTEKRLSSGQNKASGKRQRSSGIWENGRGPTPATP  2950
2951  ESFSKKSKKAVMSGIHPAEDTEGTEFEPEGLPEVVKKGFADIPTGKTSPY  3000
3001  ILRRTTMATRTSPRLAAQKLALSPLSLGKENLAESSKPTAGGSRSQKVKV  3050
3051  AQRSPVDSGTILREPTTKSVPVNNLPERSPTDSPREGLRVKRGRLVPSPK  3100
3101  AGLESNGSENCKVQ                                      3114

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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