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

NucPred

Fetching P17672 from www.uniprot.org...

The NucPred score for your sequence is 0.89 (see score help below)

   1  MVCAMQEVAAVQHQQQQQQLQLPQQQQQQQQTTQQQHATTIVLLTGNGGG    50
  51  NLHIVATPQQHQPMHQLHHQHQHQHQHQQQAKSQQLKQQHSALVKLLESA   100
 101  PIKQQQQTPKQIVYLQQQQQQPQRKRLKNEAAIVQQQQQTPATLVKTTTT   150
 151  SNSNSNNTQTTNSISQQQQQHQIVLQHQQPAAAATPKPCADLSAKNDSES   200
 201  GIDEDSPNSDEDCPNANPAGTSLEDSSYEQYQCPWKKIRYARELKQRELE   250
 251  QQQTTGGSNAQQQVEAKPAAIPTSNIKQLHCDSPFSAQTHKEIANLLRQQ   300
 301  SQQQQVVATQQQQQQQQQHQHQQQRRDSSDSNCSLMSNSSNSSAGNCCTC   350
 351  NAGDDQQLEEMDEAHDSGCDDELCEQHHQRLDSSQLNYLCQKFDEKLDTA   400
 401  LSNSSANTGRNTPAVTANEDADGFFRRSIQQKIQYRPCTKNQQCSILRIN   450
 451  RNRCQYCRLKKCIAVGMSRDAVRFGRVPKREKARILAAMQQSTQNRGQQR   500
 501  ALATELDDQPRLLAAVLRAHLETCEFTKEKVSAMRQRARDCPSYSMPTLL   550
 551  ACPLNPAPELQSEQEFSQRFAHVIRGVIDFAGMIPGFQLLTQDDKFTLLK   600
 601  AGLFDALFVRLICMFDSSINSIICLNGQVMRRDAIQNGANARFLVDSTFN   650
 651  FAERMNSMNLTDAEIGLFCAIVLITPDRPGLRNLELIEKMYSRLKGCLQY   700
 701  IVAQNRPDQPEFLAKLLETMPDLRTLSTLHTEKLVVFRTEHKELLRQQMW   750
 751  SMEDGNNSDGQQNKSPSGSWADAMDVEAAKSPLGSVSSTESADLDYGSPS   800
 801  SSQPQGVSLPSPPQQQPSALASSAPLLAATLSGGCPLRNRANSGSSGDSG   850
 851  AAEMDIVGSHAHLTQNGLTITPIVRHQQQQQQQQQIGILNNAHSRNLNGG   900
 901  HAMCQQQQQHPQLHHHLTAGAARYRKLDSPTDSGIESGNEKNECKAVSSG   950
 951  GSSSCSSPRSSVDDALDCSDAAANHNQVVQHPQLSVVSVSPVRSPQPSTS  1000
1001  SHLKRQIVEDMPVLKRVLQAPPLYDTNSLMDEAYKPHKKFRALRHREFET  1050
1051  AEADASSSTSGSNSLSAGSPRQSPVPNSVATPPPSAASAAAGNPAQSQLH  1100
1101  MHLTRSSPKASMASSHSVLAKSLMAEPRMTPEQMKRSDIIQNYLKRENST  1150
1151  AASSTTNGVGNRSPSSSSTPPPSAVQNQQRWGSSSVITTTCQQRQQSVSP  1200
1201  HSNGSSSSSSSSSSSSSSSSSTSSNCSSSSASSCQYFQSPHSTSNGTSAP  1250
1251  ASSSSGSNSATPLLELQVDIADSAQPLNLSKKSPTPPPSKLHALVAAANA  1300
1301  VQRYPTLSADVTVTASNGGPPSAAASPAPSSSPPASVGSPNPGLSAAVHK  1350
1351  VMLEA                                               1355

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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