 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9LJF3 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MKQQWQFLILCLLVLFLTVDSRGRRLLSDDVNDTALLTAFKQTSIKSDPT 50
51 NFLGNWRYGSGRDPCTWRGVSCSSDGRVIGLDLRNGGLTGTLNLNNLTAL 100
101 SNLRSLYLQGNNFSSGDSSSSSGCSLEVLDLSSNSLTDSSIVDYVFSTCL 150
151 NLVSVNFSHNKLAGKLKSSPSASNKRITTVDLSNNRFSDEIPETFIADFP 200
201 NSLKHLDLSGNNVTGDFSRLSFGLCENLTVFSLSQNSISGDRFPVSLSNC 250
251 KLLETLNLSRNSLIGKIPGDDYWGNFQNLRQLSLAHNLYSGEIPPELSLL 300
301 CRTLEVLDLSGNSLTGQLPQSFTSCGSLQSLNLGNNKLSGDFLSTVVSKL 350
351 SRITNLYLPFNNISGSVPISLTNCSNLRVLDLSSNEFTGEVPSGFCSLQS 400
401 SSVLEKLLIANNYLSGTVPVELGKCKSLKTIDLSFNALTGLIPKEIWTLP 450
451 KLSDLVMWANNLTGGIPESICVDGGNLETLILNNNLLTGSLPESISKCTN 500
501 MLWISLSSNLLTGEIPVGIGKLEKLAILQLGNNSLTGNIPSELGNCKNLI 550
551 WLDLNSNNLTGNLPGELASQAGLVMPGSVSGKQFAFVRNEGGTDCRGAGG 600
601 LVEFEGIRAERLEHFPMVHSCPKTRIYSGMTMYMFSSNGSMIYLDLSYNA 650
651 VSGSIPLGYGAMGYLQVLNLGHNLLTGTIPDSFGGLKAIGVLDLSHNDLQ 700
701 GFLPGSLGGLSFLSDLDVSNNNLTGPIPFGGQLTTFPLTRYANNSGLCGV 750
751 PLPPCSSGSRPTRSHAHPKKQSIATGMSAGIVFSFMCIVMLIMALYRARK 800
801 VQKKEKQREKYIESLPTSGSSSWKLSSVHEPLSINVATFEKPLRKLTFAH 850
851 LLEATNGFSADSMIGSGGFGDVYKAKLADGSVVAIKKLIQVTGQGDREFM 900
901 AEMETIGKIKHRNLVPLLGYCKIGEERLLVYEYMKYGSLETVLHEKTKKG 950
951 GIFLDWSARKKIAIGAARGLAFLHHSCIPHIIHRDMKSSNVLLDQDFVAR 1000
1001 VSDFGMARLVSALDTHLSVSTLAGTPGYVPPEYYQSFRCTAKGDVYSYGV 1050
1051 ILLELLSGKKPIDPEEFGEDNNLVGWAKQLYREKRGAEILDPELVTDKSG 1100
1101 DVELLHYLKIASQCLDDRPFKRPTMIQVMTMFKELVQVDTENDSLDEFLL 1150
1151 KETPLVEESRDKEP 1164
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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