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

NucPred

Fetching O57539 from www.uniprot.org...

The NucPred score for your sequence is 0.97 (see score help below)

   1  MSGLGENSLDPLASETRKRKPSSCDTPGPGLTCSGEKRRREQESKYIEEL    50
  51  ADLISANLSDIDNFNVKPDKCAILKETVRQIRQIKEQGKASSNDDDVQKA   100
 101  DVSSTGQGVIDKDSLGPLLLQALDGFLYVVNREGSIVFVSENVTQYLQYK   150
 151  QEDLVNTSVYSILHEEDRKDFLKNLPKSTVNGVPWFSETPRQKSHTFNCR   200
 201  MLVKTSHDHLEDGSNLDARQRYETMQCFALSQPRAMIEEGEDLQSCMICV   250
 251  ARRITTAERAFSANPESFITRHDLTGKVVNIDANSLRSSMRPGFEDTIRR   300
 301  CIQRFLFHSEGQPWTYKRHYQEAYVHGLSETPLYRFSLADGTMVTAQTKS   350
 351  KLFRNPVTNDPHGFVSTHFLQREQNGYRPNPNPMAQGIRPQMNPNLPNTM   400
 401  NSMPPQAMQQQNRNYGMGDPNSMAQMQGMRYKSPGNMAPVNQAPGVQQSP   450
 451  YQNNSNYGLNMNSPPHGSPGMNANQPNLMVSPRNRASPKMASNQFSPVPG   500
 501  MNSPMGSSGNAGGGSFSSSSLSALHAISEGVGSSLLSSLSSPGQKVENNS   550
 551  NMNMPQQGKICNQDCKSPSGLYCEQGQVESSVCQSSGREHLGEKDVKENI   600
 601  FEGSESQRSQAESKGHKKLLQLLTCFTEERGQSLMSSSSMDCKDSSNVTS   650
 651  PSGVSSSTSIGVSSTSNLHGSMLQEKHRILHKLLQNGNSPAEVAKITAEA   700
 701  TGKDVFQETVSSAPCTEATVKREQLSPKKKENNALLRHLLDKDDWKDPLA   750
 751  KDIKPKVEHMDIKMGSCSSSNVPTSSQDKEVKIKTEPGEEVPGDLDNLDA   800
 801  ILGDLAGSDFYSNSMSSRASDLGPKQPVFQDSPTLAMRSPDSMQGSRPPF   850
 851  NRAMSLDSRSSTPPVRNVNSFPMLPKQGMIGSPRMMDGQDNFGVMMGSGP   900
 901  NRSMNQHPGGDWAMQNSAVNRLEPPNVGSVGRPGPDYSSAMTRPAMGGNM   950
 951  PGLLTRSNSIPGSRPVMQQQQHILPMRPNDMAMSMGSNPYGQQAPSNPPG  1000
1001  SWPDAIMMNQGRGGAQNRQLGRNSLDDLLCPPSTVEGQTDEIALLDQLHT  1050
1051  LLSNTDATGLEEIDRALGIPDLVSQGQALEPQPDSYQPQGSPVMIDQKPP  1100
1101  MYGQHYAGQGAAMSAGGFNNMQGQHPPFNTVMGQMNQQQGMHPLQGMHPR  1150
1151  ANLIRPRNNIPKQLRMQLQQRLQGQQFLNQNRQALEMKVDPMNPGGAGVM  1200
1201  RPVMQTPVSQQGFLNAQMVAQKNRELISHQIRQHRMAMMMQQQQGQPQAF  1250
1251  SPPPNVTASASMDNPLGGPPMPQAPPQQFSYPPNYGINQQTDPTFGRVSS  1300
1301  PPNAMMSSRMAPSQNPHPQTTQMYPSPDMKGWPSGNMARPNSFPQQQYSH  1350
1351  QTNPATYNMMHMNGNGNHMGQMNINSLPMSGMPMGPDQKYC           1391

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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