 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UKP5 from www.uniprot.org...
The NucPred score for your sequence is 0.76 (see score help below)
1 MEILWKTLTWILSLIMASSEFHSDHRLSYSSQEEFLTYLEHYQLTIPIRV 50
51 DQNGAFLSFTVKNDKHSRRRRSMDPIDPQQAVSKLFFKLSAYGKHFHLNL 100
101 TLNTDFVSKHFTVEYWGKDGPQWKHDFLDNCHYTGYLQDQRSTTKVALSN 150
151 CVGLHGVIATEDEEYFIEPLKNTTEDSKHFSYENGHPHVIYKKSALQQRH 200
201 LYDHSHCGVSDFTRSGKPWWLNDTSTVSYSLPINNTHIHHRQKRSVSIER 250
251 FVETLVVADKMMVGYHGRKDIEHYILSVMNIVAKLYRDSSLGNVVNIIVA 300
301 RLIVLTEDQPNLEINHHADKSLDSFCKWQKSILSHQSDGNTIPENGIAHH 350
351 DNAVLITRYDICTYKNKPCGTLGLASVAGMCEPERSCSINEDIGLGSAFT 400
401 IAHEIGHNFGMNHDGIGNSCGTKGHEAAKLMAAHITANTNPFSWSACSRD 450
451 YITSFLDSGRGTCLDNEPPKRDFLYPAVAPGQVYDADEQCRFQYGATSRQ 500
501 CKYGEVCRELWCLSKSNRCVTNSIPAAEGTLCQTGNIEKGWCYQGDCVPF 550
551 GTWPQSIDGGWGPWSLWGECSRTCGGGVSSSLRHCDSPAPSGGGKYCLGE 600
601 RKRYRSCNTDPCPLGSRDFREKQCADFDNMPFRGKYYNWKPYTGGGVKPC 650
651 ALNCLAEGYNFYTERAPAVIDGTQCNADSLDICINGECKHVGCDNILGSD 700
701 AREDRCRVCGGDGSTCDAIEGFFNDSLPRGGYMEVVQIPRGSVHIEVREV 750
751 AMSKNYIALKSEGDDYYINGAWTIDWPRKFDVAGTAFHYKRPTDEPESLE 800
801 ALGPTSENLIVMVLLQEQNLGIRYKFNVPITRTGSGDNEVGFTWNHQPWS 850
851 ECSATCAGGVQRQEVVCKRLDDNSIVQNNYCDPDSKPPENQRACNTEPCP 900
901 PEWFIGDWLECSKTCDGGMRTRAVLCIRKIGPSEEETLDYSGCLTHRPVE 950
951 KEPCNNQSCPPQWVALDWSECTPKCGPGFKHRIVLCKSSDLSKTFPAAQC 1000
1001 PEESKPPVRIRCSLGRCPPPRWVTGDWGQCSAQCGLGQQMRTVQCLSYTG 1050
1051 QASSDCLETVRPPSMQQCESKCDSTPISNTEECKDVNKVAYCPLVLKFKF 1100
1101 CSRAYFRQMCCKTCQGH 1117
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
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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