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

NucPred

Fetching P41001 from www.uniprot.org...

The NucPred score for your sequence is 0.81 (see score help below)

   1  MAKNKTIEERYQKKSQIEHILLRPDTYIGSVEMHTQLLWVWNKEKNRMVQ    50
  51  KNITYVPGLYKIFDEIIVNAADVKAREKEKSENPMTCIKIEINKENKRIS   100
 101  VYNDGEGIPVDIHKEMNIYVPHMIFGELLTSDNYDDAEDRITGGRNGFGA   150
 151  KLTNIFSKEFIVQCGDSSRKKEFKMTWSDNMSKFSEPHIKNYNGKDYVKV   200
 201  TFKPDLNKFGMTEMDDDIESLLFKRVYDLAGTCSVRVYLNGQRLAVKDFK   250
 251  SYVDLYLKDNSNDNKNNKGQNDNNNNNNNNNDENANQNNDNLDVSLSNEP   300
 301  ADGTPTKNNNNNNNNNDEDEIVKIHEKQHRWEIVVSKSDGSQFQQVSFVN   350
 351  SICTTKGGSHVNYIVEQLLSSLSKKANAKNKGGMEIKSGHIRNHLWVFVN   400
 401  CLIVNPTFDSQTKETLTTKPVKFGSKCILSDKTINNVLKSPILSNILLWA   450
 451  QAKAQVELKKKMKAGSSKARERIIGIPKLEDANDAGSKYSQECTLILTEG   500
 501  DSAKTSCLAGLSIVGRDKYGVFPLKGKLLNVRDASFKQLMDNKEIQNIFR   550
 551  IMGLDITDKNKDDIKGLRYGSLMIMTDQDYDGSHIKGLLINMIHKFWPSL   600
 601  LKHKGFLSEFVTPIVKVQKGSQEYSFFTIAEYEQWKENTNLLGWKIKYYK   650
 651  GLGTSTDREFKQYFSDIKNHKIMFLWTGDRDGDSIDMAFSKKRIEDRKLW   700
 701  LQNFILGSYVDHKEKDLSYYDFVNKELIYYSRYDTERSIPNIMDGWKPGQ   750
 751  RKVLYGCFKRNLRNECKVAQLVGYIAEHSAYHHHGESSLQQTIINMAQTF   800
 801  VGSNNINFLEPCGQFGSRKEGGKDASAARYIFTKLASSTRSIFNEYDDPI   850
 851  LKYLNEEGQKIEPQYYIPVIPTILVNGCEGIGTGYSSFIPNYNYKDIIDN   900
 901  IKRYINKEPLIPMVPWYKDFKGRIESNGKTGYETIGIINKIDNDTLEITE   950
 951  LPIKKWTQDYKEFLEELLTDEKHQLILDYIDNSSHEDICFTIKMDPAKLQ  1000
1001  KAEEEGLEKVFKLKSTLTTTNMTLFDPNLKLQRYSTELDILKEFCYQRLK  1050
1051  AYENRKSYLISKLEKEKRIISNKTKFILAIVNNELIVNKKKKKVLVEELY  1100
1101  RKGYDPYKDINKIKKEEIFEQELLDAADNPEDNEEIIAGITVKDYDYLLS  1150
1151  MPIFSLTLEKVEDLLTQLKEKERELEILRNITVETMWLKDIEKVEEAIEF  1200
1201  QRNVELSNREESNKFKVARKQGPSSMKKKKKKKKLSSDEESEGGDTSDSS  1250
1251  EFLVNTLNIKKNTNKKTTTSSNNVNNSKKRLRKADDLNSNELDNTLSVSK  1300
1301  TFDDNNNLTDNTPLINRLNDENNEFSSNNVDNKSTNKNSRKKKPKIADST  1350
1351  NDNNSELNSSIQINDNVNDDINITISPNKTINVNEFSSIKNKLLELGI    1398

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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