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

NucPred

Fetching Q9Z2I4 from www.uniprot.org...

The NucPred score for your sequence is 0.53 (see score help below)

   1  MLRYLLKTLLQMNLFADSLARDISNSSELLFGFNSSLAALNPSLLPPGDP    50
  51  SLNGSRVGPEDAMPRIVEQPPDLVVSRGEPATLPCRAEGRPRPNIEWYKN   100
 101  GARVATAREDPRAHRLLLPSGALFFPRIVHGRRSRPDEGVYTCVARNYLG   150
 151  AAASRNASLEVAVLRDDFRQSPGNVVVAVGEPAVMECVPPKGHPEPLVTW   200
 201  KKGKIKLKEEEGRITIRGGKLMMSHTFKSDAGMYMCVASNMAGERESGAA   250
 251  ELVVLERPSFLRRPINQVVLADAPVNFLCEVQGDPQPNLHWRKDDGELPA   300
 301  GRYEIRSDHSLWIDQVSSEDEGTYTCVAENSVGRAEASGSLSVHVPPQFV   350
 351  TKPQNQTVAPGANVSFQCETKGNPPPAIFWQKEGSQVLLFPSQSLQPMGR   400
 401  LLVSPRGQLNITEVKIGDGGYYVCQAVSVAGSILAKALLEIKGASIDGLP   450
 451  PIILQGPANQTLVLGSSVWLPCRVIGNPQPNIQWKKDERWLQGDDSQFNL   500
 501  MDNGTLHIASIQEMDMGFYSCVAKSSIGEATWNSWLRKQEDWGASPGPAT   550
 551  GPSNPPGPPSQPIVTEVTANSITLTWKPNPQSGATATSYVIEAFSQAAGN   600
 601  TWRTVADGVQLETYTISGLQPNTIYLFLVRAVGAWGLSEPSPVSEPVQTQ   650
 651  DSSLSRPAEDPWKGQRGLAEVAVRMQEPTVLGPRTLQVSWTVDGPVQLVQ   700
 701  GFRVSWRIAGLDQGSWTMLDLQSPHKQSTVLRGLPPGAQIQIKVQVQGQE   750
 751  GLGAESPFVTRSIPEEAPSGPPQGVAVALGGDRNSSVTVSWEPPLPSQRN   800
 801  GVITEYQIWCLGNESRFHLNRSAAGWARSVTFSGLLPGQIYRALVAAATS   850
 851  AGVGVASAPVLVQLPFPPAAEPGPEVSEGLAERLAKVLRKPAFLAGSSAA   900
 901  CGALLLGFCAALYRRQKQRKELSHYTASFAYTPAVSFPHSEGLSGSSSRP   950
 951  PMGLGPAAYPWLADSWPHPPRSPSAQEPRGSCCPSNPDPDDRYYNEAGIS  1000
1001  LYLAQTARGANASGEGPVYSTIDPVGEELQTFHGGFPQHSSGDPSTWSQY  1050
1051  APPEWSEGDSGARGGQGKLLGKPVQMPSLSWPEALPPPPPSCELSCPEGP  1100
1101  EEELKGSSDLEEWCPPVPEKSHLVGSSSSGACMVAPAPRDTPSPTSSYGQ  1150
1151  QSTATLTPSPPDPPQPPTDIPHLHQMPRRVPLGPSSPLSVSQPALSSHDG  1200
1201  RPVGLGAGPVLSYHASPSPVPSTASSAPGRTRQVTGEMTPPLHGHRARIR  1250
1251  KKPKALPYRREHSPGDLPPPPLPPPEEETSWPLGLRAAGSMSSLERERSG  1300
1301  ERRVVQAVPLGAQPLGAQRGPHPDAALLGCAAEEAWLPYGRPSFLSHGQG  1350
1351  TSTCSTAGSNSSRGSNSSRGSRGSRGPGRSRSRSRSQSQSQRPGRNRREE  1400
1401  PR                                                  1402

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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