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

NucPred

Fetching P47171 from www.uniprot.org...

The NucPred score for your sequence is 0.90 (see score help below)

   1  MSMFNALNSNIEGEQYEAEEHSRELQIEQSFNILQDALIDLKNKDFEKSD    50
  51  SKFQELFQIDVVKPDRWGMYRNSSPTLDNLRYLCYRNRGMYYHLYLENNY   100
 101  ERLNSQELVNCILKAVENLVESIQHSDADFAVTDLLARIFKSFNSVKLER   150
 151  LISEYEFTKQENLSLLLGRHRKFLLNDLTLMMNNYVELTNKLLVPNLSDN   200
 201  TIFERYHLEKYKDIKPEPLAFGPILSRISEMKKQDEEIMKKLDVFNVTLN   250
 251  EESWDEVAKALKNLLPSVKTSSLIGRNMDPYNEIEEPIEAVKFELSEAIN   300
 301  NTPSLDRESERQEEEQDNESVRADDKSGNLAPSDIQTNEEARPNKRTDEH   350
 351  IDSTKPLQRSSKRFKEREQENSKELVMDVHKRFFGEFNTLLSYIHILPFC   400
 401  DFDTFASKFIIGSSDKQPEKFIPYTDLYECLKSWSSRYTDIFNQNDYLSS   450
 451  GSNENEELFQLNALLKSNAFDDKESFPRYLNDLDSDHIRSFISEVNAGNL   500
 501  HFHQVRLKLLFKLLGTYDEGNGRRLIIDYLWESQLLKIVLWFVFGIESNI   550
 551  FALINKNKRQCKYLALSIYELLVNHLGNIVEEITNKRIQGHKSADLKSQR   600
 601  NKVEKRIRSWHTLLEQIADEKDKELYVHFQWTHYCFLQYTCDIVDSRLSE   650
 651  TLTSLENTIKDSDSSLDIAYPNYRHIPALNLNTVQSQKRKIRIIQNITVE   700
 701  DISEDTNSDTHSENHLETLEKVLLHILHPSTNHSNIDEEMVSFIFNSPFL   750
 751  LKIRLWGVLFSSYVKKSSIQDVQRIYFHVLDFMKGALTSPVYKESNPHGR   800
 801  HQMLLTVLTAIGYLSSQLTAILNSNRWESSDFVLEDYMFEKLLQTFFFFY   850
 851  TVLFYESSAVNDVSNKSFFKRASKSSGKMKDIMIDLATLILYYYDLQAKL   900
 901  RTPAEQGIETTELIWSLHTLFGHFHFCDASNGKFLDLAEKLLCQFINNDS   950
 951  FLQLKQILWCRYHYAIASDNFSPDLHDTKAVEMEKIHSLPLGTYLIKLQY  1000
1001  QNKNPYLSSSKTTLKQIMDNIIEKIGDPSTLDNHIISRNSFLLNEYLSRP  1050
1051  ITADLLKHTFSGATSLYLTSPNDELQQGMTAGLFYVSSLQSLGLYKMRKK  1100
1101  SMQARPSELDSIIRMLKNDIIYNTNRFESWILLGKCYSYIVEDDLIWTSD  1150
1151  KITVPEKKDVIALTQRKAILCYLMAISIYYSKLDRTIDDKKIILEALDDL  1200
1201  GSMLISGYYNPMNKLCFSWKSSAENTMRLSETGEVVMEKTKKITTISDFN  1250
1251  IEQSIFLCFNRACSLSGDIKSQDDVFVLNWSSFYNLAKFFFKTDGGNNCK  1300
1301  LVAKYITQGCQIAYESSPAKDPIIEPHYLLVNACYKWVKRGVIGVNEALT  1350
1351  LLSKDNQFFQEQEEFWVNDEGLAWDYQEKFFFDKIIRLLRHLLSVDKKKW  1400
1401  QHRPRYRIARILFDDLGDVNGALEEMDSLISAKSINKNLVNIWKPDFERP  1450
1451  GKHFIYTYQYLVLYLDLLFAIKDFNTTGLVIKKLRRFGSGTVNVNELLER  1500
1501  AINVYTQSAKIKLQLQDKSYVEQILPTLNYQEFLKISEQLNQVFDQGKYP  1550
1551  EEISSGLKLAFQLKKGHSGIAFDSVCLGIYFEYLYFPLARQDQSLTDVND  1600
1601  ENNPALPSSGSVTSKSTPDPTSKPSAIKKRVTKKEVFDRVRLLVDKIT    1648

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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