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

NucPred

Fetching Q69ZL1 from www.uniprot.org...

The NucPred score for your sequence is 0.86 (see score help below)

   1  MTSAAELKKPPLAPKPKLVGTNNKPPPPPIAPKPDIGSASVPRLTKKTKP    50
  51  AIAPKPKVPTNSVVQDIKHPPSKKPTLNLEEREPELPESTGKSNCKDVRD   100
 101  PHSDYILPTCSCSSGCIHEPRTRETQCVEQLVLEPLGMKENLENSKNGES   150
 151  SKRGSSWDSSSEKCRGQSGVVLKASILEEKLKEVLTQQRSPCGSPGRHRA   200
 201  PKKPEMNGDHSCTRQIRIEFADVSSSLTGFEKVPAHHNCHPQLPRDESQT   250
 251  LKTCQDGSAESRGHTDSCEPENKRVASDGISQKTEVKGLGPLEIHLLPYT   300
 301  SKFPTPKPRKTHAAARLRRQKHVDTPGESTEEPGNSNNGSSCLLEDYCLK   350
 351  NNKVSVLRQNALYNQGPVDEVRPANQRALTGDSNSGGQDSVGSQKAVQQQ   400
 401  TPSLDTDSSLTSDSSGSGVSPAVDKETTYTQCSTQPLSLPKQVTSACTDQ   450
 451  PPATCNPEVSAPPIQKESSSSRIIPKKPQRHSLPAAGVLKKAASEELVEK   500
 501  SSSGKETNVEKGLHRNYLHHPGPPNHGASASPFDMPNPTSEKPVWKLPHP   550
 551  ILPFSGSPEALKRVTLSLNNEPSVSLTKPRAKSLSAVDADRCNKPCKDPP   600
 601  KKTSFKKLINVKLSIGFIKSDFQKIRSKSCQHGDVSAGHPLAREPKGLES   650
 651  DWQGLATGEEKRSKPTKAHSAENCSLESQKVKSWGQSSAVNGQRAESLDD   700
 701  RILSRHTSCTGDFGPEYENVRHYEEIPEYENLPFVMAGRNTPDLGWQNSS   750
 751  SVEDTDASLYEVEEPYNAPDGQLQLDPRHQPCSSGTSQEGKDALHLGLSD   800
 801  LPSDEEVINSSDEDDVSSESSKGEPDPLEDKQDEDAGMKSKVHHIAKEIM   850
 851  SSEKVFVDVLKLLHIDFRGAVAHASRQLGKPVIEDRILNQILYYLPQLYE   900
 901  LNRDLLKELEERMLTWTEQQRIADIFVKKGPYLKMYSTYIKEFDKNVALL   950
 951  DEQCKKNPGFAAVVREFEMSPRCANLALKHYLLKPVQRIPQYRLLLTDYL  1000
1001  KNLLEDSVDHRDTQDALAVVIEVANHANDTMKQGDNFQKLMQIQYSLSGH  1050
1051  HEIVQPGRVFLKEGTLMKLSRKVMQPRMFFLFNDALLYTTPMQSGMYKLN  1100
1101  NMLSLAGMKVRKPTQEAYQNELKIESVERSFILSASSAAERDDWLEAISS  1150
1151  SIEEYAKKRITFCPSRSLDEDSERKEEVSPLGAKAPIWIPDTRATMCMIC  1200
1201  TSEFTLTWRRHHCRACGKIVCQACSSNKYGLDYLKGQLARVCEHCFQELQ  1250
1251  KLDHQLSPRVGSPGNHKSPSSALSSVLHSIPSGRKQKKIPAALKEVSANT  1300
1301  EDSTMSGYLYRSKGSKKPWKHLWFVIKNKVLYTYAASEDVAALESQPLLG  1350
1351  FTVTLVKDENSESKVFQLLHKGMVFYVFKADDAHSTQRWIDAFQEGTVL   1399

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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