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

NucPred

Fetching O75694 from www.uniprot.org...

The NucPred score for your sequence is 0.59 (see score help below)

   1  MPSSLLGAAMPASTSAAALQEALENAGRLIDRQLQEDRMYPDLSELLMVS    50
  51  APNNPTVSGMSDMDYPLQGPGLLSVPNLPEISSIRRVPLPPELVEQFGHM   100
 101  QCNCMMGVFPPISRAWLTIDSDIFMWNYEDGGDLAYFDGLSETILAVGLV   150
 151  KPKAGIFQPHVRHLLVLATPVDIVILGLSYANLQTGSGVLNDSLSGGMQL   200
 201  LPDPLYSLPTDNTYLLTITSTDNGRIFLAGKDGCLYEVAYQAEAGWFSQR   250
 251  CRKINHSKSSLSFLVPSLLQFTFSEDDPILQIAIDNSRNILYTRSEKGVI   300
 301  QVYDLGQDGQGMSRVASVSQNAIVSAAGNIARTIDRSVFKPIVQIAVIEN   350
 351  SESLDCQLLAVTHAGVRLYFSTCPFRQPLARPNTLTLVHVRLPPGFSASS   400
 401  TVEKPSKVHRALYSKGILLMAASENEDNDILWCVNHDTFPFQKPMMETQM   450
 451  TAGVDGHSWALSAIDELKVDKIITPLNKDHIPITDSPVVVQQHMLPPKKF   500
 501  VLLSAQGSLMFHKLRPVDQLRHLLVSNVGGDGEEIERFFKLHQEDQACAT   550
 551  CLILACSTAACDREVSAWATRAFFRYGGEAQMRFPTTLPPPSNVGPILGS   600
 601  PVYSSSPVPSGSPYPNPSFLGTPSHGIQPPAMSTPVCALGNPATQATNMS   650
 651  CVTGPEIVYSGKHNGICIYFSRIMGNIWDASLVVERIFKSGNREITAIES   700
 701  SVPCQLLESVLQELKGLQEFLDRNSQFAGGPLGNPNTTAKVQQRLIGFMR   750
 751  PENGNPQQMQQELQRKFHEAQLSEKISLQAIQQLVRKSYQALALWKLLCE   800
 801  HQFTIIVAELQKELQEQLKITTFKDLVIRDKELTGALIASLINCYIRDNA   850
 851  AVDGISLHLQDICPLLYSTDDAICSKANELLQRSRQVQNKTEKERMLRES   900
 901  LKEYQKISNQVDLSNVCAQYRQVRFYEGVVELSLTAAEKKDPQGLGLHFY   950
 951  KHGEPEEDIVGLQAFQERLNSYKCITDTLQELVNQSKAAPQSPSVPKKPG  1000
1001  PPVLSSDPNMLSNEEAGHHFEQMLKLSQRSKDELFSIALYNWLIQVDLAD  1050
1051  KLLQVASPFLEPHLVRMAKVDQNRVRYMDLLWRYYEKNRSFSNAARVLSR  1100
1101  LADMHSTEISLQQRLEYIARAILSAKSSTAISSIAADGEFLHELEEKMEV  1150
1151  ARIQLQIQETLQRQYSHHSSVQDAVSQLDSELMDITKLYGEFADPFKLAE  1200
1201  CKLAIIHCAGYSDPILVQTLWQDIIEKELSDSVTLSSSDRMHALSLKIVL  1250
1251  LGKIYAGTPRFFPLDFIVQFLEQQVCTLNWDVGFVIQTMNEIGVPLPRLL  1300
1301  EVYDQLFKSRDPFWNRMKKPLHLLDCIHVLLIRYVENPSQVLNCERRRFT  1350
1351  NLCLDAVCGYLVELQSMSSSVAVQAITGNFKSLQAKLERLH           1391

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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