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

NucPred

Fetching O09000 from www.uniprot.org...

The NucPred score for your sequence is 0.95 (see score help below)

   1  MSGLGESSLDPLAAESRKRKLPCDAPGQGLVYSGEKWRREQESKYIEELA    50
  51  ELISANLSDIDNFNVKPDKCAILKETVRQIRQIKEQGKTISSDDDVQKAD   100
 101  VSSTGQGVIDKDSLGPLLLQALDGFLFVVNRDGNIVFVSENVTQYLQYKQ   150
 151  EDLVNTSVYSILHEPRRKDFLNTYQNPQLMEFLGLMRTRDKKAPYILIVR   200
 201  MLMKTHDILEDVNASPETRQRYETMQCFALSQPRAMLEEGEDLQCCMICV   250
 251  ARRVTAPFPSSPESFITRHDLSGKVVNIDTNSLRSSMRPGFEDIIRRCIQ   300
 301  RFFSLNDGQSWSQKRHYQEAYVHGHAETPVYRFSLADGTIVSAQTKSKLF   350
 351  RNPVTNDRHGFISTHFLQREQNGYRPNPIPQDKGIRPPAAGCGVSMSPNQ   400
 401  NVQMMGSRTYGVPDPSNTGQMGGARYGASSSVASLTPGQSLQSPSSYQNS   450
 451  SYGLSMSSPPHGSPGLGPNQQNIMISPRNRGSPKMASHQFSPAAGAHSPM   500
 501  GPSGNTGSHSFSSSSLSALQAISEGVGTSLLSTLSSPGPKLDNSPNMNIS   550
 551  QPSKVSGQDSKSPLGLYCEQNPVESSVCQSNSRDPQVKKESKESSGEVSE   600
 601  TPRGPLESKGHKKLLQLLTCSSDDRGHSSLTNSPLDPNCKDSSVSVTSPS   650
 651  GVSSSTSGTVSSTSNVHGSLLQEKHRILHKLLQNGNSPAEVAKITAEATG   700
 701  KDTSSTASCGEGTTRQEQLSPKKKENNALLRYLLDRDDPSDVLAKELQPQ   750
 751  ADSGDSKLSQCSCSTNPSSGQEKDPKIKTETNDEVSGDLDNLDAILGDLT   800
 801  SSDFYNNPTNGGHPGAKQQMFAGPSSLGLRSPQPVQSVRPPYNRAVSLDS   850
 851  PVSVGSGPPVKNVSAFPGLPKQPILAGNPRMMDSQENYGANMGPNRNVPV   900
 901  NPTSSPGDWGLANSRASRMEPLASSPLGRTGADYSATLPRPAMGGSVPTL   950
 951  PLRSNRLPGARPSLQQQQQQQQQQQQQQQQQQQQQQQMLQMRTGEIPMGM  1000
1001  GVNPYSPAVQSNQPGSWPEGMLSMEQGPHGSQNRPLLRNSLDDLLGPPSN  1050
1051  AEGQSDERALLDQLHTFLSNTDATGLEEIDRALGIPELVNQGQALESKQD  1100
1101  VFQGQEAAVMMDQKAALYGQTYPAQGPPLQGGFNLQGQSPSFNSMMGQIS  1150
1151  QQGSFPLQGMHPRAGLVRPRTNTPKQLRMQLQQRLQGQQFLNQSRQALEM  1200
1201  KMENPAGTAVMRPMMPQAFFNAQMAAQQKRELMSHHLQQQRMAMMMSQPQ  1250
1251  PQAFSPPPNVTASPSMDGVLAGSAMPQAPPQQFPYPANYGMGQPPEPAFG  1300
1301  RGSSPPSAMMSSRMGPSQNAMVQHPQPTPMYQPSDMKGWPSGNLARNGSF  1350
1351  PQQQFAPQGNPAAYNMVHMNSSGGHLGQMAMTPMPMSGMPMGPDQKYC    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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