 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P51842 from www.uniprot.org...
The NucPred score for your sequence is 0.43 (see score help below)
1 MFLGPWPFSRLLSWFAISSRLSGQHGLTSSKFLRYLCLLALLPLIWWGQA 50
51 LPYKIGVIGPWTCDPFFSKALPEVAAALAIERISRDMSFDRSYSFEYVIL 100
101 NEDCQTSKALTSFISHQQMASGFVGPANPGYCEAASLLGNSWDKGIFSWA 150
151 CVNHELDNKHSYPTFSRTLPSPIRVLVTVMKYFQWAHAGVISSDEDIWVH 200
201 TANQVSSALRSHGLPVGVVLTSGQDSRSIQKALQQIRQADRIRIIIMCMH 250
251 SALIGGETQTHFLELAHDLKMTDGTYVFVPYDVLLYSLPYKHSPYQVLRN 300
301 NQKLREAYDAVLTITVESHEKTFYEAFTEAAAGGEIPEKLDSHQVSPLFG 350
351 TIYNSIYFIAQAMSNALKENGQASAASLTRHSRNMQFYGFNQLIRTDSNG 400
401 NGISEYVILDTNGKEWELRGTYTVDMETELLRFRGTPIHFPGGRPTSADA 450
451 KCWFAQGKICQGGIDPALAMMVCFALLLALLSINGFAYFIRRRINKIQLI 500
501 KGPNRILLTLEDVTFINPHFGSKRGSRASVSFQIISEVQSGRSPRLSFSS 550
551 GSLTPATYENSNIAIYQGDWVWLKKFPPGDFGDIKSIKSSASDVFEMMKD 600
601 LRHENVNPLLGFFYDSGMFAIVSEFCSRRSLEDILTQDDVKLDWMFKSSL 650
651 LLDLIKGMKYLHHREFIHGRLKSRNCVVDGRFVLKVTDYGFNNILEMLRL 700
701 SEEEPSEEELLWTAPELLRAPGGIRLGSFAGDVYSFAIIMQEVMVRGAPF 750
751 CMMDLSAKEVIDRLKMPPPVYRPVVSPEFAPPECLQLMKQCWAEAAEQRP 800
801 TFDEIFNQFKTFNKGKKTNIIDSMLRMLEQYSSNLEDLIRERTEELEIEK 850
851 QKTEKLLTQMLPPSVAESLKKGCTVEPEGFDLVTLYFSDIVGFTTISAMS 900
901 EPIEVVDLLNDLYTLFDAIIGSHDVYKVETIGDAYMVASGLPKRNGSRHA 950
951 AEIANMSLDILSSVGTFKMRHMPEVPVRIRIGLHTGPVVAGVVGLTMPRY 1000
1001 CLFGDTVNTASRMESTGLPYRIHVSLSTVTILRTLSEGYEVELRGRTELK 1050
1051 GKGTEETFWLVGKKGFTKPLPVPPPVGKDGQVGHGLQPAEIAAFQRRKAE 1100
1101 RQLVRNKP 1108
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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