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

NucPred

Fetching P30432 from www.uniprot.org...

The NucPred score for your sequence is 0.84 (see score help below)

   1  MSNTTRSSRVTIGRIGTTPQITDPWSSGLEKQRPSRCGGPKSLAEPTYRK    50
  51  IGRRKMMLHMRVHDPGTTVTQRAKETATAKLNRIYLCTFNRMAQSCIYFV   100
 101  LFLVILSPNTSCALRSSAGETQNYVGILSNDSATTTYDVSSLHSSRRTNP   150
 151  PSSSSSSSSNVDVDYRNDRELHKVDLVGLGGERAGQAETISGGKYDYNYE   200
 201  NTHTNASAKDEIVERQSNSLDFDGVDMFGAFSIPEEAIYTNEFAVNIPAG   250
 251  KQMADVIATKHGFINRGQIGSLDNYYLFQHHHVSKRSLRSSRKHQGALKS   300
 301  ENEVKWMQQQHEKVRRKRDGPYQDLPTYSPYNLLRQHGGYVVDPNPHLSF   350
 351  SPESISLASHSQRMEYRDVSSHFIFPDPLFKEQWYLNGGAKDGLDMNVGP   400
 401  AWQKGYTGKGVVVSILDDGIQTNHPDLAQNYDPEASFDINGNDSDPTPQD   450
 451  NGDNKHGTRCAGEVAAVAFNNFCGVGVAYNASIGGVRMLDGKVNDVVEAQ   500
 501  ALSLNPSHIDIYSASWGPEDDGSTVDGPGPLARRAFIYGVTSGRQGKGSI   550
 551  FVWASGNGGRYTDSCNCDGYTNSIFTLSISSATQAGFKPWYLEECSSTLA   600
 601  TTYSSGTPGHDKSVATVDMDGSLRPDHICTVEHTGTSASAPLAAGICALA   650
 651  LEANPELTWRDMQYLVVYTSRPAPLEKENGWTLNGVKRKYSHKFGYGLMD   700
 701  AGAMVSLAEQWTSVPPQHICKSRENNEDRKIDGAYGSTLSTHMDVNGCAG   750
 751  TINEVRYLEHVQCRITLRFFPRGNLRILLTSPMGTTSTLLFERPRDIVKS   800
 801  NFDDWPFLSVHFWGEKAEGRWTLQVINGGRRRVNQPGILSKWQLIFYGTS   850
 851  TQPMRLKSELLNSSPQLRSPSSSNPFLFPSASNIGQPANEGGNFNTDSFA   900
 901  SYLNYQNIFSSAGSDPEPATATLDGQNVTAAIAGGSSAESLGFTASAAQL   950
 951  VAAPETRDGDKKILHSCDAECDSSGCYGRGPTQCVACSHYRLDNTCVSRC  1000
1001  PPRSFPNQVGICWPCHDTCETCAGAGPDSCLTCAPAHLHVIDLAVCLQFC  1050
1051  PDGYFENSRNRTCVPCEPNCASCQDHPEYCTSCDHHLVMHEHKCYSACPL  1100
1101  DTYETEDNKCAFCHSTCATCNGPTDQDCITCRSSRYAWQNKCLISCPDGF  1150
1151  YADKKRLECMPCQEGCKTCTSNGVCSECLQNWTLNKRDKCIVSGSEGCSE  1200
1201  SEFYSQVEGQCRPCHASCGSCNGPADTSCTSCPPNRLLEQSRCVSGCREG  1250
1251  FFVEAGSLCSPCLHTCSQCVSRTNCSNCSKGLELQNGECRTTCADGYYSD  1300
1301  RGICAKCYLSCHTCSGPRRNQCVQCPAGWQLAAGECHPECPEGFYKSDFG  1350
1351  CQKCHHYCKTCNDAGPLACTSCPPHSMLDGGLCMECLSSQYYDTTSATCK  1400
1401  TCHDSCRSCFGPGQFSCKGCVPPLHLDQLNSQCVSCCQNQTLAEKTSSAA  1450
1451  CCNCDGETGECKATSTGGKRRTVVGSGSAYKSSESKHGSFENDGNAREFV  1500
1501  LRLDSPLTAITAIAVAICLLIITIFSIIFAVLQRNSNHVSRNSVRYRKIA  1550
1551  NTSSGRRKNLSAKPTSDARFIFNIGEDDDTDGDNSDDELDGNVGTDINNR  1600
1601  IVYDRKGNDHGHEFYIESTNDIDAIEFHCNGAGAQKAETQLQRCNANGDD  1650
1651  DDILHYDRHTNAERKNHPSSTTSRTNIRS                       1679

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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