 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O02740 from www.uniprot.org...
The NucPred score for your sequence is 0.32 (see score help below)
1 MFLAPWPFSHLMLWFVTLGRQRGQHGLASFKLLWCLWLLVLMSLPLQVWA 50
51 PPYKIGVVGPWTCDPLFSKALPEIAAQLATERINKDPALDLGHSLEYVIF 100
101 NEDCQASRALSSFISHHQMASGFIGPANPGYCEAASLLGNSWDKGIFSWA 150
151 CVNYELDSKNSHPTFSRTLPSPIRVLLTVMKYFQWAHAGVISSDEDIWVH 200
201 TAYRVASALRSRGLPVGVVLTTGQDSQSIQKALQQIRQADRIRIIIMCMH 250
251 STLIGGETQTHLLEWAHDLQMTDGTYVFVPYDTLLYSLPYKHTPYKVLRN 300
301 NPKLREAYDAVLTITVESQEKTFYQAFEEAAARGEIPEKLESDQVSPLFG 350
351 TIYNSIYFIAQAMNNAMKENGWASAASLVQHSRNVQFYGFNQLIRTDANG 400
401 NGISEYVILDTNWKEWELHSTYTVDMETELLRFGETPIHFPGGRPPRADA 450
451 QCWFADGRICQGGINPTFALMVCLALLIALLSINGFAYFIRHRINKIQLI 500
501 KGPNRILLTLEDVTFINPHFGSKRGSHASVSFQITSEVQSGRSPRLSFSS 550
551 GSLTPATCENSNIAIYEGDWVWLKKFPSGNFGDIKSVESSASDIFEMMKD 600
601 LRHENINPLVGFFYDSGVFAIVTEFCSRRSLEDILMNQDVKLDWMFKSSL 650
651 LLDLIKGMKYLHHREFAHGRLKSRNCVVDGRFVLKVTDYGFNDILETLRL 700
701 SQEEPSAEELLWTAPELLRAPRGSRLRSFAGDVYSFAIIMQEVMVRGTPF 750
751 CMMDLPAKEIIERIKKPPPVYRPVVPPEHAPPECLQLMKQCWAEAAEQRP 800
801 TFDEIFNQFKTFNKGKKTNIIDSMLRMLEQYSSNLEDLIQERTEELEIEK 850
851 QKTEKLLTQMLPPSVAESLKKGCTVEPEGFDLVTLYFSDIVGFTTISAMS 900
901 EPIEVVDLLNDLYTLFDAIIGSHDVYKVETIGDAYMVASGLPKRNGMRHA 950
951 AEIANMSLDILSSVGTFKMRHMPEVPVRIRIGLHSGPVVAGVVGLTMPRY 1000
1001 CLFGDTVNTASRMESTGLPYRIHVSHSTVTILRTLGEGYEVELRGRTELK 1050
1051 GKGTEETFWLVGKKGFTKPLPVPPPVGKDGQVGHGLQSVEIAAFQRRKQK 1100
1101 SSW 1103
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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