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

NucPred

Fetching Q91690 from www.uniprot.org...

The NucPred score for your sequence is 0.80 (see score help below)

   1  MASRQCPPAAVFNSMNPPVNSYVEHCYLRSPNVMAEGMNEMPYCHQTNLM    50
  51  TSHHGFGLAQGSDHLAGTDGSRFSTPRSTMKLSKKRAMSISPLSDASIDL   100
 101  QTMIRTSPNSLVAFINSRCSSASGSYGHLSIGTISPSLGYQNCLNHQRPQ   150
 151  AGPYGSNPLMPYNSHEHLSSRGMSMLQPRSSVKHCQLKSEPLSITGLDTI   200
 201  GSKRLEDGSEGDISSPASVGTQDPLLGLLDGRDDLEKDDGKHEPETVYET   250
 251  NCHWESCTKEFDTQEHLVHHINNEHIHGEKKEFVCHWQDCSRELRPFKAQ   300
 301  YMLVVHMRRHTGEKPHKCTFEGCNKAYSRLENLKTHLRSHTGEKPYVCEH   350
 351  EGCNKAFSNASDRAKHQNRTHSNEKPYVCKIPGCTKRYTDPSSLRKHVKT   400
 401  VHGPEAHITKKHRGDGMLRAQPGHEGPGNQNVKGENQLDMEASSACKEDG   450
 451  RLAVPDITLKSQPSPGGQSSCSSERSPLGSTNNNDSGVEMNANTGGSFED   500
 501  LTNLDDIPSVDSMGTAGASALRKLENLRIDKLNQLRKTPSSGKMVKLPSI   550
 551  HNSGPQGDMSVVCGPLGMSHNQHGIELPASSHVNHLNDRRNSTTSTMSSA   600
 601  YTVSRRSSVVSPYLPNQRAGDSGNMVDSYDISTDPSGHSNEAVCASGLPG   650
 651  LTPAQQYRLKAKYAAATGGPPPTPLPNMERMNTNNRMAFASSDYRGSAIS   700
 701  SLQRRHSSNEYHNYGTGIIHPAQAPGAGIRRASDPARTGGDIQAVPKVQR   750
 751  FKSMTNMNVSMMGRQGTSIQQAYGGSDANLQRHMFSPRPPSITENVFMET   800
 801  AGPDEVCHTKEQGFIQSNEMQHYMNYQGQGSQLTAPNDHMNFNPQIHGLD   850
 851  GQSQNVYSHSQRAISNMHLNAENYSGQSNVSNFNQCQMTAHNQHFQNTRQ   900
 901  AYNCANLPVQWNEVSSGTMDNPVQRPNHQIMHQNMSPGNHCQSQLSNCTV   950
 951  PESTKQGCPVNRNSCQQGMYMNNQKYNHGGQVQVKPEQQFHHSAPAMMSC  1000
1001  QNMKHPSRQEHHFTKTNTMPLSSEATNCDYQGQQDSTQNSCFNVGLNLNL  1050
1051  LSPPGRRSQTPIMQVKEIMVRNYVQSQQALMWEQHPKSMAMMTNSGDDVD  1100
1101  TRQNQHKNTLNAAVYMGPKYMNYQGKPSPNNLMSPSSQDSQSSHTKAMGS  1150
1151  PSSQCYNFDMMPHPPCGPKPLSRQHSVSSQSTYMGSPNQLSPSYQSSESS  1200
1201  PRRMACLPPIQPQSEVTNNTSMMYYTGQMEMHQSKPGVHKLTTPLNLNQT  1250
1251  SCDGHQHGQYNASHSFLKTVPYTSSCPAANTLDSLDLENTQIDFTAIIDD  1300
1301  ADNALMPGNISPNVLAGSSQASSHLTTLRNTGAVVPNMVVGDLNSMLSSL  1350
1351  AGENKYLNTM                                          1360

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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