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

NucPred

Fetching Q6NVD0 from www.uniprot.org...

The NucPred score for your sequence is 0.39 (see score help below)

   1  MASRARRTAKFSSFQPILAQSPRLLLLLLLLSLVSYVSTQAAGPGAALQS    50
  51  LGLSGTSGVPTEEAIVVANRGLRVPFGREVWLDPLRDLVLQVQPGDRCTV   100
 101  TVLDNDALAQRPGHLSPKRFACDYGPGEVRYSHLGARSPSRDRVRLQLRY   150
 151  DAPGGAIVLPLALEVEVVFTQLEIVTRNLPLVVEELLGTSNALDDRSLEF   200
 201  AYQPETEECRVGILSGLSALPRYGELLHYPQVQGGAGDRGTSKTLLMDCK   250
 251  AFQELGVRYRHTAPSRSPNRDWLPMVVELHSRGAPEGSPALKREHFQVLV   300
 301  RIRGGAENTAPKPSFVAMMMMEVDQFVLTALTPDMLAAEDAESDPDLLIF   350
 351  NLTSAFQPGQGYLVSTDDRSLPLSSFTQRDLRLLKIAYQPPSEDSDQERL   400
 401  FELELEIVDPEGAASDPFAFMVVVKPMNTLAPVVTRNTGLILYEGQYRPL   450
 451  TGPIGSGPQNLVISDEDDLEAVRLEVVAGLRHGHLVILGSPSSDSAPKTF   500
 501  TVAELAAGQVVYQHDDKDGSLSDNLVLRMSDGGGRHQVQFLFPITLVPVD   550
 551  DQPPVLNANTGLTVAEGETVPIPPLTLSATDIDSDDSQLVFVLLPPFSSL   600
 601  GHLLLRQRHVPQEEQGLWQKQGSFYERTVTEWRQQDITEGKLFYRHSGPH   650
 651  SPGPVMDQFMFRVQDNHDPPNQSGIQRFVIRIHPVDRLPPELGSGCPLRM   700
 701  VVQESQLTPLRKRWLHYTDLDTDDRELQYTVTQPPTDTDENHSPAPLGTL   750
 751  VFTDNPSVVVSHFTQAQVNHHKIAYRPPGQELGVAARVAQFQFQVEDRAG   800
 801  NVAPGTFTLYLQPVDNQPPEIVNTGFTVEEKGHHILRETELHVSDVDTDV   850
 851  THISFTLTQAPKHGHMQISGRPLHVGGQFHLEDIKHGRISYWNSGDESLT   900
 901  DSCSLEVSDRHHVVPITLRVNVRPGDREGPMSVLPAGTLESYLDVLENGA   950
 951  TEVTANIIKGAYQGTDDLMLTFLLEGPPSYGEILVNGAPAEQFTQRDILE  1000
1001  GSVVYAHTSGEIGLLPKADSFNLSLSAMSQEWRIGSSIVQGVTVWVTILP  1050
1051  VDSQAPEISLGEQFVVLEGDKSVISLTHLSAEDMDSLKDDLLCTIVIQPT  1100
1101  SGYVENISPAPGSEKSRAGVAISAFTLKDLRQGHINYVQSVHRGVEPVED  1150
1151  RFIFRCSDGINFSERQIFPIVIIPTNDEQPEMFMREFMVMEGMSLVVNRL  1200
1201  ILNAADADIPRDDLTFTITRFPTHGHVMNQLINGTVLVESFTLDQIIESS  1250
1251  SIIYEHDDSETQEDSFVIKLTDGKHSVEKMVLIVVIPVDDETPRMTINNG  1300
1301  LEIEIGETKVINNKVLMATDLDSDDKSLVYIIRYGPGHGLLQRQKPLGAF  1350
1351  ENITLGMNFTQDEVDRNLIQYVHFGQEGIRDLIKFDVTDGTNALIDRYFY  1400
1401  VTIGSVDIVFPDVVSKGVSLKEGGKVTLTTDLLSTSDLNSPDENLVFTIT  1450
1451  RAPMRGHLECTDRRGLSITSFTQLQLAGNKIYYIHTAEDEVKMDSFEFQV  1500
1501  TDGRNPVFRTFRISISDVDNKKPVVTIHNLVVSESESKLITPFELTVEDR  1550
1551  DTPDRLLKFIVTQVPVHGHLLFNNTRSVMVFTKQDLNENLISYKHDGTES  1600
1601  TEDSFSFTVTDGTHSDFYVFPDTVFETRRPQVMKIQVLPVDNSVPQIVVN  1650
1651  KGASTLRTLATGHLGFMITSKILKVEDRDSLHFSLRFIVTEAPQHGYLLN  1700
1701  LGQGNHSVTQFTQADIDDMKICYVLRERANATSDMFHFIVEDDGGNRLTN  1750
1751  QHFRLNWAWISFEKEYYLINEDSKFLDIVLTRRGYLGETSFISIGTRDGT  1800
1801  AEKDRDFKGKAQKQVQFNPGQTRASWRVRILSDGEHEHSETFQVVLSEPV  1850
1851  LAILEFPTVTTVEIIDPGDESTVFIPQSEYSVEEDVGELFIPIRRSGDIS  1900
1901  RELMVICYTQQGTATSTVRTSVLSYSDYISRPEDHSSVIRFDKDEREKMC  1950
1951  RILVIDDSLYEEEETFQVLLSMPMGGRIGDKFPGANVTILTDRDDEPAFY  2000
2001  FGDTQYSVDESAGYVELQVWRTGTDLSKPSSVTVRSRKTESLSADAGTDY  2050
2051  VGISRNLDFAPGVNMQTVRVVILDDLGRPILEGIEKFELVLRMPMNAALG  2100
2101  EPSKATVSINDSASDLPKMQFKERVYTCNENDGRVVAMIYRSGDIQHRSS  2150
2151  VRCYTRQGSAQVMMDFEERPNTDVSTVTFLPGEMEKPCVLELMDDAVYED  2200
2201  VEELRLVLGTPQGSSAFGAAVGEQNETLIKIQDEADKAVIKFGETKFSVT  2250
2251  EPSRPGESVVVKIPVIRQGDTSKVSIVRVHTKDGSATSGEDYHPVSEEIE  2300
2301  FKEGETQHTVEIEVIFDGVREMREAFTVHLKPDENMVAETQATKAIVYIE  2350
2351  EIHSMADVTFPSVPHIVSLLIYDDPSKGREDTGPVSGYPVVCITACNPKY  2400
2401  PDYEKTGSICASENINDTLTRYRWLISAPAGPDGVTSPMREVDFDTFFTS  2450
2451  SKMITLDSIYFQPGSRVQCAARAVNTNGNEGLELMSPIVTIGREEGLCQP  2500
2501  RVPGVVGAEPFSAKLRYTGPEDPDFANLIKLTVTMPHIDGMLPAISTREL  2550
2551  SNFELTLSPDGTRVGNHKCSNLLDYNEVKTHHGFLTNATKNPEVIGETYP  2600
2601  YQYSVPVRGSSTLRFYRNLNLEACLWEFVSYYDMTELLADCGGTIGTDGQ  2650
2651  VLNLVQSYVTLRVPLYVSYVFHSPVGVGGWQHFDLKSELRLTFVYDTAIL  2700
2701  WNHGIGSPPEAELQGSLYPTSMRIGEEGRLAVNFKTEAQFHGLFVLSHPA  2750
2751  SFTSSLIVSADHPGLTFSLRLIRSEPTYNQPVQQWSFVSDFAVRDYSGTY  2800
2801  TVKLVPCTTPSNQEYRLPVTCNPREPVTFDLDIRFQQVSDPVATEFSLNT  2850
2851  HMYLLSKKNLWLSDGSMGFGQESDVAFAEGDVIYGRVMVDPVQNLGDSFY  2900
2901  CSIEKVFLCTGDDGYVPKYSPANAEYGCLADSPSLLHRFKIVDKAQPETQ  2950
2951  ATSFGDVLFNAKLAVDDPEAVLLVNQPGSDGFKVDSTPLFQVALGREWYI  3000
3001  HTIYTVKSKDNTHRGIGKRSLEYQYHSVVHPGPPQATTKSWKKRAVRSTP  3050
3051  SLAGEIGAENNRGTNIQHISLNRRGKRQVPHGRIPPDGILPWELNSPSSE  3100
3101  VSLVTVLGGLTVGLLTVCLAVAAAVMCRNRSTKGKDTPKGSGSTEPMMSP  3150
3151  QSHYNDSSEV                                          3160

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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