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

NucPred

Fetching Q4VZP3 from www.uniprot.org...

The NucPred score for your sequence is 0.84 (see score help below)

   1  MEVLMAERADLVFHNKVIDGTAIKRLISRLIDHFGMAYTSHILDQLKTLG    50
  51  FQQATATSISLGIDDLLTIPSKGWLVQDAEQQSLILEKHHHYGNVHAVEK   100
 101  LRQSIEIWYATSEYLRQEMNPNFRMTDPFNPVHIMSFSGARGNASQVHQL   150
 151  VGMRGLMSDPQGQMIDLPIQSNLREGLSLTEYIISCYGARKGVVDTAVRT   200
 201  SDAGYLTRRLVEVVQHIVVRRTDCGTIRGILVSPGNRMIPERIFIQTLIG   250
 251  RVLADDIYMGPRCIGVRNQDIGIGLINRFITFQTQPISIRTPFTCRSTSW   300
 301  ICRLCYGRSPTHGDLVELGEAVGIIAGQSIGEPGTQLTLRTFHTGGVFTG   350
 351  GTAEHIRAPSNGKIKFNEDLVHPTRTRHGHPAFLCYIDLYVTIESEDIIH   400
 401  NVTIPPKSLLLVQNDQYVESEQVIAEIRAGTYTLNLKERVRKHIYSDSEG   450
 451  EMHWSTDVYHAPEFTYSNVHLLPKTSHLWILSGGSCGCSLVPFSLYKDQD   500
 501  QINVHSLCVERRYISSLSVNNDKVGQKFYGPDLSGKNENGIPDYSELNPI   550
 551  LCTGQSNLTYPAIFHGNSDLLAKRRRNGFIIQFESLQEREKELRPPSGIS   600
 601  IEIPINGIFRRNSILAFFDDPQYRRNSSGITKYGTIGVHSILKKEDLIEY   650
 651  RGVKDFKPKYQMQMKVDRFFFIPEEVHILPESSSIMVRNNSIIGVATRLT   700
 701  LSIRSRVGGLVRVEKKKKRIELKIFSGDIHFPGEMDKISRHNGILIPPER   750
 751  VKKNSKKSKKSKNWIYVQWITPTKKKYFVFVRPVIIYELADGINLVKLFP   800
 801  QDLLQERDNLELRVANYILYGNGKPIRGISGTSIQLVRTCLLLNWDRDKK   850
 851  SSSIEDARASFVEVSTNGLVRNFLRIDLGKSDTAYIRKRKDPSGSGLIFN   900
 901  NESDRTNINPFFSIYSKTRVPQSPSQNQGTIRTLLNRNKERQSLIILSAS   950
 951  NCLQIDLFNDVKDYNVIKESSKKDPLISIRNSLGPLGAAPQIVNFYSFYY  1000
1001  LITHNPISLTKYLQLDNLKQIFQVLKYYLMDENGGILNSDPCINIVFNTF  1050
1051  NLNWHFLHDNYHNNYCEETPTRISLGQFFFENVCIAKNRPHLKSGQIIIV  1100
1101  QVDSLVIRSAKPYLATSGATVHRHYGEILYEGDTLVTFIYEKSRSGDITQ  1150
1151  GLPKVEQVLEVRSIDSISMSLEKRIEGWNERITRILGIPWGFLIGAELTI  1200
1201  VQSRISLVNKIQKVYRSQGVEIHNRHIEIIVRQITSKVLVSEDGMSNVFS  1250
1251  PGELIGLLRAERTGRALEEAICYRAVLLGITKASLNTQSFISEASFQETA  1300
1301  RVLAKAALRGRIDWLRGLKENVVLGGMIPVGTGFRELAHRSRQHNNIPLE  1350
1351  TPPKKIFEGEMRDILFHHKELFDFFISTNLHDTSEQEFLGFNDS        1394

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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