 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UTT1 from www.uniprot.org...
The NucPred score for your sequence is 0.40 (see score help below)
1 MVLSNVDAEEVNMDSSMELEESSQEPLRADNYEEIYNSLVHHEPDLEEAA 50
51 HASYSWVVKNFSTLEDKTYSPLFKAGHTTWRIVLFPKGCNQTEYASVFLE 100
101 YLPQCKVEAIRKYEAELAAGKTPTIDPEIVNDETYSCCAQFALSLSNVQD 150
151 PTVMQINTSHHRFRSEVKDWGFTRFVDLRKIAVPTPEFPVPFLENDEICI 200
201 SVTVRVLQDPTGVLWHSFVNYNSKKETGYVGLKNQGATCYMNSLLQSLFF 250
251 TNIFRKTVYKIPTDNDDSRDSVAYALQRVFYNLEKQREPVSTTELTRSFG 300
301 WNSFDSFMQHDIQEFNRVLQDNLEKKMKGTEVENALNDIFVGKMKSYVKC 350
351 IDVNYESSRVEDFWDIQLNVKGMDTLEDSFRDAIQVETLTGDNKYYAEGH 400
401 GLQDAHKGIIFESLPNVLQLQLKRFDYDMLRDMMVKINDRHEFPLEIDLE 450
451 PYLSETADKSESHVYVLHGVLVHGGDLHGGHYYALIKPEKDSNWFKFDDD 500
501 RVTRATIKEVLEDNYGGEPAGRAKGYNGNPFKRFMNAYMLVYFRKSRLDH 550
551 ILSPVTAEDVPFHVRNTLDEEHRVVERKLLEREEQQIYRRVRVLTTDGFK 600
601 KYHGFDMTDFSASDDDPVLITTKIKRNANIWDLQKHLAGLLNRDTSGIRI 650
651 WLMTNRQNRTVRVDLPLDKKTILVDQICDMHIRKDMDMRVYVEFLSEHNQ 700
701 LLADFGATDDNDFDTYIFLKIFDYETQQISGLADLHVSKNSPISSLSEWI 750
751 REHLKWSSDVPITYYEEIKTGMVDVLDPNASFEKSEIQVGDIICFEKKLV 800
801 HDSSSDTSHPYKSALDLYDFMAHRVVITFEPRYSDDTNNGVFDLVLTTHT 850
851 NYTDMARAVANKLNVDPNYLQFTMAHLPSRTPRSVIRNPSKFTLQNAIPS 900
901 TYSHNQNVVMFYEVLDITLSELERKQLIRVHFLSNGISHETQMEFYVDKE 950
951 GTVEDILRQVTQKVPLNAEDASRLRLYEVYNHRILKSHLPTDGIYDLNEF 1000
1001 STAYVEVTPKEEQMQLKTDDAVSIVVQHFFKDLSRLHDIPFYFVLLRGET 1050
1051 LKDLKKRLQKRLGYNDTQFSKVKLAVLQAQSFGKPYYLTDDDEVLYGELE 1100
1101 PQSHILGLDHPPANGSAQYHGMDQAIRMK 1129
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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