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

NucPred

Fetching Q7YJY0 from www.uniprot.org...

The NucPred score for your sequence is 0.77 (see score help below)

   1  MEVLMAERADLVFHNKVIDGAAMKRLISRLIDHFGMAYTSHILDQVKTLG    50
  51  FQQATTTSISLGIDDLLTIPSKGWLVQDAEQQSLILEKHHHYGNVHAVEK   100
 101  LRQSIEIWYATSEYLRQEMHPNFRMTDPSNPVHIMSFSGARGNASQVHQL   150
 151  VGMRGLMSDPQGQMIDLPIQSNLREGLSLTEYIISCYGARKGVVDTAVRT   200
 201  SDAGYLTRRLVEVVQHIVVRRTDCGTIRGISVSPRNGMTERIWIQTLIGR   250
 251  VLAYDIYMGPRCIAARNQDIGIGLVNRFITFRAQPIYIRTPFLCRSTSWI   300
 301  CRLCYGRSPAHGDLVELGEAVGIIAGQSIGEPGTQLTLRTFHTGGVFTGG   350
 351  TAEHVRAPFNGKIQFNEDLVHPTRTRHGHPAFLCYTDLYVTIESHDILHN   400
 401  VNIPQKSFLLVQNDQYVESEQVIAEIRAGTSTFNLKVKEKVRKHIYSDSE   450
 451  GEMHWSTDVYHAPEYTYGNVHLLPKTSHLWILSGGLCRSSIVPFSLHKDQ   500
 501  DQMNVHSLSVEQRYISDLSVTNVRVRHKLFSSDSSGKKGGRAPDYSGPDR   550
 551  IISNGHWNFIYPAILHENSDLLAKRRRNRFIIPFQSDQEREKELMPRSGI   600
 601  SIEIPINGILRRNSILAYFDDPRYRRSSSGITKYGTIGVDSIVKKEDLIE   650
 651  YRRAKEFRPKYQMKVDRFFFIPEEVHILPASSPVMVRNNSIIGVNTRIAL   700
 701  NTRSRVGGLVRVERKKKRIELKIFSGDIHFPGETDKISRHSGILIPPGTG   750
 751  KKNVKESKKWKNWIYVQRITPTKKKYFVLVRPVVTYEIADGINLETLFPQ   800
 801  DPLQERDNVQLRVVNYILYGNGKPIRGISHTSLQLVRTCLVLNWDQDRNG   850
 851  SIEEVHASFVEVRANGLIRDFLKIDLVKSPILYSGKRNDTTSSGFIPNNG   900
 901  SDRTNINPFYFKARIQSLTQHQGTIRTLLNRNKECQSFLILSSSDCSRIG   950
 951  SFNGSKSHKVTKESITIKEDPTIPIRNSLGPLGTVPKIANFDSSYYLITH  1000
1001  NQILLNKYLSLDNLKQTFQVLKYYLMDENGRIYNPDPRSNIIFNPFDLNW  1050
1051  CFLPHDYCEETSTIINPGQFICENVCISKYGPHIKSGQILIVRVDSLVIR  1100
1101  SAKPHLATPGATVHGHCGEILYEGDTLVTFIYEKSRSGDITQGLPKVEQV  1150
1151  LEARSIDSISMNLEKRVEGWNERITKILGIPWGFLIGAELTIAQSRISLV  1200
1201  NKIQKVYRSQGVQIHNRHIEIIVRQITSRVLVSEDGMSNVFSPGELIGLL  1250
1251  RAERTGRALEEAICYRAILLGITRASLNTQSFISEASFQETARVLAKAAL  1300
1301  RGRIDWLKGLKENVVLGGMMPVGTGFKGLVRRSRQHNNIPLEIKKKNLFE  1350
1351  GEMRDILFHHRELFDSCIPNNFPDTLE                         1377

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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