 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q63406 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MSNCWCFIFCKERVRSNSSSPQHDGTSREEADHQVDVSDGIRLVPDKAEA 50
51 TMATASDEIMHQDIVPLCAADIQEQLKKRFAYLSGGRGQDGSPVITFPDY 100
101 PAFSEIPDKEFQNVMTYLTSIPSLQDAGIGFILVIDRRQDKWTSVKASVL 150
151 RIAASFPANLQLVLVLRPTGFFQRTLSDIAFKFNRDEFKMKVPVMMLSSV 200
201 PELHGYIDKSQLTEDLGGTLDYCHSRWLCHRTAIESFALMVKQTAQMLQA 250
251 FGTELAETELPNDVQSTSLVLSAHTEKKAKVKEDLQLALTEGNSILESLR 300
301 EPLAESIVHSVNQDQLDNQATVKRLLTQLNETEAAFDEFWAKHQQKLEQC 350
351 LQLRHFEQGFREVKTALDSMSQKIAAFTDVGNSLAHVQHLLKDLTTFEEK 400
401 SSVAVDKARALSLEGQQLIENRHYAVDSIHPKCEELQHLCDHFASEVTRR 450
451 RDLLSKSLELHSLLETSMKWSDEGIFLLASQPVDKCQSQDGAEAALQEIE 500
501 KFLETGAENKIQELNKIYKEYECILNQDLLEHVQKVFQKQESTEEMFHRR 550
551 QASLKKLAAKQTRPVQPVAPRPEALTKSPSPSPGSWRSSENSSSEGNALR 600
601 RGPYRRAKSEMSEPRQGRTSSTGEEEESLAILRRHVMNELLDTERAYVEE 650
651 LLCVLEGYAAEMDNPLMAHLISTGLQNKKNILFGNMEEIYHFHNRIFLRE 700
701 LESCIDCPELVGRCFLERMEEFQIYEKYCQNKPRSESLWRQCSDCPFFQE 750
751 CQKKLDHKLSLDSYLLKPVQRITKYQLLLKEMLKYSKHCEGAEDLQEALS 800
801 SILGILKAVNDSMHLIAITGYDGNLGDLGKLLMQGSFSVWTDHKKGHTKV 850
851 KELARFKPMQRHLFLHEKAVLFCKKREENGEGYEKAPSYSYKQSLNMTAV 900
901 GITENVKGDTKKFEIWYNAREEVYIIQAPTPEIKAAWVNEIRKVLTSQLQ 950
951 ACREASQHRALEQSHSLPLPTPASTSPTKGSTRNVKKLEDRKTDPLCLEG 1000
1001 CVSSSLPKPPEKGKGWSKTSHSLEAPEEDGGWSSAEELINSSDAEEDGGV 1050
1051 GPRKLVPGKYTVLMDGEKGGSDTLAMRSGDMVEVVEEGTEGLWYVRDLTS 1100
1101 SKEGWVPASSLATLLGKSSSAQCLSSSGKTHCARQLCPEPAKILSPEPV 1149
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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