 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P24814 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MDQDNNNHNDSNRLHPPDIHPNLGPQLWLNSSGDFDDNNNNNNNNNNNNS 50
51 TRPQMPSRTRETATSERNASEVRDATLNNIFRFDSIQRETLLPTNNGQPL 100
101 NQNFSLTFQPQQQTNALNGIDINTVNTNLMNGVNVQIDQLNRLLPNLPEE 150
151 ERKQIHEFKLIVGKKIQEFLVVIEKRRKKILNEIELDNLKLKELRIDNSP 200
201 QAISYLHKLQRMRLRALETENMEIRNLRLKILTIIEEYKKSLYAYCHSKL 250
251 RGQQVENPTDNFIIWINSIDTTESSDLKEGLQDLSRYSRQFINNVLSNPS 300
301 NQNICTSVTRRSPVFALNMLPSEILHLILDKLNQKYDIVKFLTVSKLWAE 350
351 IIVKILYYRPHINKKSQLDLFLRTMKLTSEETVFNYRLMIKRLNFSFVGD 400
401 YMHDTELNYFVGCKNLERLTLVFCKHITSVPISAVLRGCKFLQSVDITGI 450
451 RDVSDDVFDTLATYCPRVQGFYVPQARNVTFDSLRNFIVHSPMLKRIKIT 500
501 ANNNMNDELVELLANKCPLLVEVDITLSPNVTDSSLLKLLTRLVQLREFR 550
551 ITHNTNITDNLFQELSKVVDDMPSLRLIDLSGCENITDKTIESIVNLAPK 600
601 LRNVFLGKCSRITDASLFQLSKLGKNLQTVHFGHCFNITDNGVRALFHSC 650
651 TRIQYVDFACCTNLTNRTLYELADLPKLKRIGLVKCTQMTDEGLLNMVSL 700
701 RGRNDTLERVHLSYCSNLTIYPIYELLMSCPRLSHLSLTAVPSFLRPDIT 750
751 MYCRPAPSDFSENQRQIFCVFSGKGVHKLRHYLVNLTSPAFGPHVDVNDV 800
801 LTKYIRSKNLIFNGETLEDALRRIITDLNQDSAAIIAATGLNQINGLNND 850
851 FLFQNINFERIDEVFSWYLNTFDGIRMSSEEVNSLLLQVNKTFCEDPFSD 900
901 VDDDQDYVVAPGVNREINSEMCHIVRKFHELNDHIDDFEVNVASLVRVQF 950
951 QFTGFLLHEMTQTYMQMIELNRQICLVQKTVQESGNIDYQKGLLIWRLLF 1000
1001 IDKFIMVVQKYKLSTVVLRLYLKDNITLLTRQRELLIAHQRSAWNNNNDN 1050
1051 DANRNANNIVNIVSDAGANDTSNNETNNGNDDNETENPNFWRQFGNRMQI 1100
1101 SPDQMRNLQMGLRNQNMVRNNNNNTIDESMPDTAIDSQMDEASGTPDEDM 1150
1151 L 1151
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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