 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O02671 from www.uniprot.org...
The NucPred score for your sequence is 0.47 (see score help below)
1 MTCPKFSVALLHWEFIYVITAFDLAYPITPWKFKLSCMPPNTTYDFLLPA 50
51 GISKNTSTLNGHDEAVVETELNSSGTYLSNLSSKTTFHCCFWSEEDKNCS 100
101 VHADNIAGKAFVSAVNSLVFQQTGANWNIQCWMKEDLKLFICYMESLFKN 150
151 PFKNYDLKVHLLYVLLEVLEGSPLLPQKGSFQSVQCNCSARECCECHVPV 200
201 SAAKLNYTLLMYLKITSGGAVFHSPLMSVQPINVVKPDPPLGLHMEITDT 250
251 GNLKISWSSPTLVPFQLQYQVKYSENSTTNMREADEIVSDTSLLVDSVLP 300
301 GSSYEVQVRGKRLDGPGIWSDWSTPFTFTTQDVIYFPPKILTSVGSNISF 350
351 HCIYKNENKIVSSKKIVWWMNLAEKIPQSQYDVVGDHVSKVTFPNMNATK 400
401 PRGKFTYDAVYCCNEHECHHRYAELYVIDVNINISCETDGYLTKMTCRWS 450
451 TNAIQSLVGSTLQLRYHRSSLYCSDVPSVHPISEPKDCQLQRDGFYECIF 500
501 QPIFLLSGYTMWIRINHPLGSLDSPPTCVIPDSVVKPLPPSSVKAEITAK 550
551 IGLLKISWEKPVFPENNLQFQIRYGLSGKEVQWKIYEVYDTKLKSTSLPV 600
601 PDLCAVYAVQVRCKRLDGLGYWSNWSTPAYTVVTDVKVPIRGPEFWRIIN 650
651 EDATKKERNITLLWKPLMKNDSLCSVRSYVVKHHTSRHGTWSEDVGNHTK 700
701 LTFLWTEQAHSVTVLAVNSIGASSANFNLTFSWPMSKVNIVQSLSAYPLN 750
751 SSCVGLSWLLSPSDYNLMYFILEWKILNEDHEIKWLRIPSSVKKYYIHDH 800
801 FIPIEKYQFSLYPIFMEGVGKPKIINSFTQDGEKHRNDAGLYVIVPIIIS 850
851 SSILLLGTLLMSHQRMKKLFWEDVPNPKNCSWAQGLNFQKPETFEHLFIK 900
901 HTESVTFGPLLLEPETISEDISVDTSWKNKDEMVPPTTVSLLLTTPDLEK 950
951 SSICISDQRSSAHFSEAESMEITREDENRRQPSIKYATLLSSPKSGETEQ 1000
1001 EQELVSSLVSRCFSSSNSLPKESFSNSSWEIETQAFFILSDQHPNMTSPH 1050
1051 LSFSEGLDELMKFEGNFPKEHNDERSVYYLGVTSIKKRESDVFLTDESRV 1100
1101 RCPFPAHCLFADIKILQESCSHLVENNFNLGTSGQKTFVSYMPQFQTCST 1150
1151 QTQKIMENKMYDLTV 1165
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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