 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02763 from www.uniprot.org...
The NucPred score for your sequence is 0.21 (see score help below)
1 MDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWR 50
51 PHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAY 100
101 FCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKE 150
151 EDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFT 200
201 SAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRT 250
251 CEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNE 300
301 ACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIQRMTP 350
351 KIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNH 400
401 TDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNA 450
451 PNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNE 500
501 IVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLN 550
551 LLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSV 600
601 LLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNI 650
651 THSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLK 700
701 GLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIA 750
751 ILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGT 800
801 LALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRM 850
851 DAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYL 900
901 AIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVAR 950
951 GMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRL 1000
1001 PVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEK 1050
1051 LPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERK 1100
1101 TYVNTTLYEKFTYAGIDCSAEEAA 1124
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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