 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P283 from www.uniprot.org...
The NucPred score for your sequence is 0.76 (see score help below)
1 MPCGFSPSPVAHHLVPGPPDTPAQQLRCGWTVGGWLLSLVRGLLPCLPPG 50
51 ARTAEGPIMVLAGPLAVSLLLPSLTLLVSHLSSSQDVSSEPSSEQQLCAL 100
101 SKHPTVAFEDLQPWVSNFTYPGARDFSQLALDPSGNQLIVGARNYLFRLS 150
151 LANVSLLQATEWASSEDTRRSCQSKGKTEEECQNYVRVLIVAGRKVFMCG 200
201 TNAFSPMCTSRQVGNLSRTIEKINGVARCPYDPRHNSTAVISSQGELYAA 250
251 TVIDFSGRDPAIYRSLGSGPPLRTAQYNSKWLNEPNFVAAYDIGLFAYFF 300
301 LRENAVEHDCGRTVYSRVARVCKNDVGGRFLLEDTWTTFMKARLNCSRPG 350
351 EVPFYYNELQSAFHLPEQDLIYGVFTTNVNSIAASAVCAFNLSAISQAFN 400
401 GPFRYQENPRAAWLPIANPIPNFQCGTLPETGPNENLTERSLQDAQRLFL 450
451 MSEAVQPVTPEPCVTQDSVRFSHLVVDLVQAKDTLYHVLYIGTESGTILK 500
501 ALSTASRSLHGCYLEELHVLPPGRREPLRSLRILHSARALFVGLRDGVLR 550
551 VPLERCAAYRSQGACLGARDPYCGWDGKQQRCSTLEDSSNMSLWTQNITA 600
601 CPVRNVTRDGGFGPWSPWQPCEHLDGDNSGSCLCRARSCDSPRPRCGGLD 650
651 CLGPAIHIANCSRNGAWTPWSSWALCSTSCGIGFQVRQRSCSNPAPRHGG 700
701 RICVGKSREERFCNENTPCPVPIFWASWGSWSKCSSNCGGGMQSRRRACE 750
751 NGNSCLGCGVEFKTCNPEGCPEVRRNTPWTPWLPVNVTQGGARQEQRFRF 800
801 TCRAPLADPHGLQFGRRRTETRTCPADGSGSCDTDALVEVLLRSGSTSPH 850
851 TVSGGWAAWGPWSSCSRDCELGFRVRKRTCTNPEPRNGGLPCVGDAAEYQ 900
901 DCNPQACPVRGAWSCWTSWSPCSASCGGGHYQRTRSCTSPAPSPGEDICL 950
951 GLHTEEALCATQACPEGWSPWSEWSKCTDDGAQSRSRHCEELLPGSSACA 1000
1001 GNSSQSRPCPYSEIPVILPASSMEEATDCAGFNLIHLVATGISCFLGSGL 1050
1051 LTLAVYLSCQHCQRQSQESTLVHPATPNHLHYKGGGTPKNEKYTPMEFKT 1100
1101 LNKNNLIPDDRANFYPLQQTNVYTTTYYPSPLNKHSFRPEASPGQRCFPN 1150
1151 S 1151
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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