 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UNY4 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MEEVRCPEHGTFCFLKTGVRDGPNKGKSFYVCRADTCSFVRATDIPVSHC 50
51 LLHEDFVVELQGLLLPQDKKEYRLFFRCIRSKAEGKRWCGSIPWQDPDSK 100
101 EHSVSNKSQHASETFHHSSNWLRNPFKVLDKNQEPALWKQLIKGEGEEKK 150
151 ADKKQREKGDQLFDQKKEQKPEMMEKDLSSGLVPKKKQSVVQEKKQEEGA 200
201 EIQCEAETGGTHKRDFSEIKSQQCQGNELTRPSASSQEKSSGKSQDVQRE 250
251 SEPLREKVTQLLPQNVHSHNSISKPQKGGPLNKEYTNWEAKETKAKDGPS 300
301 IQATQKSLPQGHFQERPETHSVPAPGGPAAQAAPAAPGLSLGEGREAATS 350
351 SDDEEEDDVVFVSSKPGSPLLFDSTLDLETKENLQFPDRSVQRKVSPASG 400
401 VSKKVEPSDPVARRVYLTTQLKQKKSTLASVNIQALPDKGQKLIKQIQEL 450
451 EEVLSGLTLSPEQGTNEKSNSQVPQQSHFTKTTTGPPHLVPPQPLPRRGT 500
501 QPVGSLELKSACQVTAGGSSQCYRGHTNQDHVHAVWKITSEAIGQLHRSL 550
551 ESCPGETVVAEDPAGLKVPLLLHQKQALAWLLWRESQKPQGGILADDMGL 600
601 GKTLTMIALILTQKNQEKKEEKEKSTALTWLSKDDSCDFTSHGTLIICPA 650
651 SLIHHWKNEVEKRVNSNKLRVYLYHGPNRDSRARVLSTYDIVITTYSLVA 700
701 KEIPTNKQEAEIPGANLNVEGTSTPLLRIAWARIILDEAHNVKNPRVQTS 750
751 IAVCKLQACARWAVTGTPIQNNLLDMYSLLKFLRCSPFDEFNLWRSQVDN 800
801 GSKKGGERLSILTKSLLLRRTKDQLDSTGRPLVILPQRKFQLHHLKLSED 850
851 EETVYNVFFARSRSALQSYLKRHESRGNQSGRSPNNPFSRVALEFGSEEP 900
901 RHSEAADSPRSSTVHILSQLLRLRQCCCHLSLLKSALDPMELKGEGLVLS 950
951 LEEQLSALTLSELRDSEPSSTVSLNGTFFKMELFEGMRESTKISSLLAEL 1000
1001 EAIQRNSASQKSVIVSQWTNMLKVVALHLKKHGLTYATIDGSVNPKQRMD 1050
1051 LVEAFNHSRGPQVMLISLLAGGVGLNLTGGNHLFLLDMHWNPSLEDQACD 1100
1101 RIYRVGQQKDVVIHRFVCEGTVEEKILQLQEKKKDLAKQVLSGSGESVTK 1150
1151 LTLADLRVLFGI 1162
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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