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

NucPred

Fetching Q49L08 from www.uniprot.org...

The NucPred score for your sequence is 0.88 (see score help below)

   1  MEVLMAERVNLVFHNKVIDGTAIKRLISRLIDHFGMAYTSHILDQVKTLG    50
  51  FQQATATSISLGIDDLLTIPSKGWLVQDAEQQSLILEKHHHYGNVHAVEK   100
 101  LRQSIEVWYATSEYLRQEMNPNFRMTDPFNPVHIMSFSGARGNASQVHQL   150
 151  VGMRGLMSDPQGQMIDLPIQSNLREGLSLTEYIISCYGARKGVVDTAVRT   200
 201  SDAGYLTRRLVEVVQHIVVRRTDCGTIRGVSVSPRNGMMPERIFIQTLIG   250
 251  RVLADDIYMGPRCIAIRNQDIGIGLVNRFITFRTQPISIRTPFTCRSTSW   300
 301  ICRLCYGRSPTHGDLVELGEAVGIIAGQSIGEPGTQLTLRTFHTGGVFTG   350
 351  GTAEHVRAPSNGKIKFNDDLVHPTRTRHGHPAFLCSIDLYVTIESEDILH   400
 401  NVTIPPKSFLLVQNNQYVESEQVIAEIRAGTYTFNLKERVRKHIYSESEG   450
 451  EMHWSTDVYHAPEFTYSNVHLLPKTSHLWILSGGSCRSSRVPFSLYKDQD   500
 501  QMNLRSTERERRYISSLSVNNDQMRYELCSSDFSGKIKEDRIPDYSELNR   550
 551  IISIVHCNLKYPTTFDENSDLLAKRRRNRFLIPLQSIQERKKELMPHSGI   600
 601  SIELPINGILRRNSILAYFDDPRYRRKSSGIIKYGTLGVHSVVKKEDLIE   650
 651  YRGIKEFKQKCQMKLDPFFFIPEEVHIFPESSSIMVRNNSLIGVDTRIAL   700
 701  NTRSRVGGLVRVERKKKRIELKIFSGDIHFPGETDKISRHSGILIPPGTG   750
 751  KTNFKESKKWKNWIYVQRITPTKKKYFVLVRPVVTYEVLDGINLATLFPP   800
 801  DLLQERDNMQLRVVNYIVYRNGKPIRGISDTSIQLVRTCLILNWDQDKKS   850
 851  SSIEEAHTSFVEVSTNGLIRDFLRIDLVKFPISYLRKRNDPSGSGLISDS   900
 901  DNVSDHTNSNPFYSKTKIQQLLSQNQGTIRTLLNKNKECPSLIILSSSNC   950
 951  FRMGPFNAGKYHNVIKESIKKDPIIKIRNSIGPLGTVLQFVNFYSFYYLI  1000
1001  THNPILVTKYLQLENLKQTFQVINYYLMDENGRILNPDSCSNIVLNSFNL  1050
1051  NWYFLHHNYYHNYFEERSTIISLGQFICENVCISKNGPHLKSGQVLIVQV  1100
1101  DSVVIRSAKPYLATPGATVHGHYGEILYEGDTLVTFIYEKSRSGDITQGL  1150
1151  PKVEQVLEVRSIDSISMNLEKRIEGWNEHIKKILGIPWGFLIGAELTIVQ  1200
1201  SRISLVNKIQKVYRSQGVQIHNRHIEIIVRQITSKVLVSEDGMSNVFLPG  1250
1251  ELIGLLRAARTGRALEESICYRAILLGITRASLNTQSFISEASFQETARV  1300
1301  LAKAALRGRIDWLKGLKENVVIGGMIPVGTGFKGLVHRSRQHKNIPLKTK  1350
1351  KKNFFEGEIGDILFHHRELFDSSISKKFHDTSEQSFRGFNDS          1392

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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