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

NucPred

Fetching Q68S16 from www.uniprot.org...

The NucPred score for your sequence is 0.83 (see score help below)

   1  MEVLMAERANLVFHNKVIDGTAMKRLISRLIEHFGMAYTSHILDQVKTLG    50
  51  FQQATATSISLGIDDLLTIPSKRWLVQDAEQQSLILEKQHHYGNVHAVEK   100
 101  LRQSIEIWYATSEFLRQEMKPNFRMTDPFNPVHIMSFSGARGNASQVHQL   150
 151  VGMRGLMADPQGQMIDLPIQSNLREGLSLTEYIISCYGARKGVVDTAVRT   200
 201  SDAGYLTRRLVEVVQHIVVRRKDCGTVRGISVSPRNGMMPERVFIQTLIG   250
 251  RVLADDIYLGPRCIATRNQDIGSGLVNRFITFRAQPIYIRTPFTCRSTSW   300
 301  ICRLCYGRSPTHGDLAELGEAVGIIAGQSIGEPGTQLTLRTFHTGGVFTG   350
 351  GTAEHVRAPSNGKIKFNEDLVHPTRTRHGHPAFLCSIDLYVTIESEDIIH   400
 401  NVNIPPKSFLLVQNDQYVESEQVIAEIRAGTSTLNFKEKVRKHIYSDSEG   450
 451  EMHWNTDVYHAPAFTYGNVHLLPKTSHLWILLGEPCRSDLVALSIHKDQD   500
 501  QMNAHSLSVKRRYISNLSVTNDQARHKFFSSDFSGKKEDRITDYSELNRI   550
 551  GHCNLPYLAILHANSDLLAKRRRNRFIIPLQSIQEHENELMPSSGISIEI   600
 601  PIHGIFRKKSIIAYFDDPRYRRKSSGITKYGTIEVHSIVKKEDLIEYRGV   650
 651  KEFRPKYQMKVDRFFFIPEEVHILPGSSSIMVRNNSIIGVDTQITLTTRS   700
 701  RVGGLVRVERKKKRIELKIFSGDIHFPGETDKISRHSGVLIPPGTGKTNS   750
 751  KESKKWKNWIYVQRITPTKKKYFVLVRPVVTYEITDGINLATLFFPDLLQ   800
 801  ERDNVQLRVVNYILYGTGKPIRGFYDTSIQLVRTCLVLNWVQDKKSSSIE   850
 851  EARTSFVEIRINSLIRDFLKIDLAKSPILYTGKRKDPSGSGLISENGSDR   900
 901  TNINPFSSSFFYSKARIKESLNQNQGTIHTLLNRNKECQSLIILSSSNCF   950
 951  RMGPFNDVKYHNVIKESIQKDPLIQIRNSLGPLGTALQIANFYSFSHLIT  1000
1001  YNQILVTNYLQLDNLKQSFQVLKYYLMDENGKIYNPDRCCNIILNPFNLN  1050
1051  WFFLHHNYCEETSTTMSLGQFICENVCIAKNGPHLKPGQVLIVQVDSVVI  1100
1101  RSAKPYLATPGATVHGHYGEILYEGDTLVTFIYEKSRSGDITQGLPKVEQ  1150
1151  VLEVRSIDSISMNLEKRVEGWNECITRNLGIPWGFLIGAELTIVQSRISL  1200
1201  VNKIQKVYRSQGVQIHNRHIEIIVRQITSKVLVSEDGMSNVFLPGEFIGL  1250
1251  LRAERMGRALEEAICYQAVLLGITKASLNTQSFISEASFQETARVLAKAA  1300
1301  LRGRIDWLKGLKENVVLGGMIPVGTGFKGLVPPSRQHKNIPLETKNKNLF  1350
1351  EGEMRDILFHHRKFFDSCLSKNFHDTPEQSFIGFNDS               1387

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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