 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P42499 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MLQQAERRIPPFRRRKSTPHEQRLSHHSSNNNNNIDSMSKAIAQYTEDGV 50
51 HAVFEQSGESGRSFNYSESIRIASESVPEQQITAYLVKIQRGGFIQPFGS 100
101 MIAVDEPSFRILGYSDNARDMLGITPQSVPSLDDKNDAAFALGPQSVPSL 150
151 DDKNDAAFALGTDVRALFTHSSALLLEKAFSAREISLMNPIWIHSRTSGK 200
201 PFYGILHRIDVGIVIDLEPARTEDPALSIAGAVQSQEALVRAISQLQSLP 250
251 SADVKLLCDTVVESVRELTGYDRVMVYKFHEDEHGEVVSESKRPDLEPYI 300
301 GLHYPATDIPQASRFLFKQNRVRMIVDCHASAVRVVQDEALVQPLCLVGS 350
351 TLGAPHGCHAQYMANMGSIASLVMAVIINGNDEEGVGGRSSMRLWGLVVC 400
401 HHTSARCIPFPLRYACEFLMQAFGLQLNMELQLAAQSLEKRVLRTQTLLC 450
451 DMLLRDSPTGIVTQSPSIMDLVKCDGAALYFQGNYYPLGVTPTEAQIRDI 500
501 IEWLLAFHGDSTGLSTDSLGDAGYPGLPRLGMQFVGWQVAYITEKDFLFW 550
551 FRSHTAKEIKWGGAKLILRTRMMGQRMHPLSSFKAFLEVVKSRSLPWENA 600
601 EMDAIHSLQLILRDSFKDAEHRNSKAVVDPHVSEQELQGVDELSSVAREM 650
651 VRLIETATAPIFAVDVDGHVNGWNAKVSELTGLPVEEAMGKSLVHDLVFK 700
701 ESEETVNKLLSREEDKNVETKMRTFGKEHQNKAAFLVVNACSSKHFTNNV 750
751 VGVCFVGQNVTGQKIVMHKFINIQGDYKAIVHSPNPLIPPIFASDDNTCC 800
801 LEWNTAMEKLDPSNENVTVGGVDVIGKMLVGEVFGSCCQLKGSDSITKFM 850
851 IVLHNALGGQDTDKFPFSFLDRHGKYVQTFLTANKRVNMEGQIIGAFCFL 900
901 QIMSPELQQALKAQRQQEKEFLGRMKELAYICQGVKKPLSGIRFTNSLLE 950
951 ATSLTNEQKQFLETSVACEKQMLKIIRDVDLESIEDGSLELEKGEFLLGN 1000
1001 VINAVVSQVILLLRERNLQLIRDIPEEIKTLAVYGDQLRIQQVLSDFLLN 1050
1051 IVRYAPSPDGWVEIHVRPRIKQISDGLTLLHAEFRMVCPGEGLPPELIQD 1100
1101 MFNNSRWGTQEGLGLSMSRKILKLMNGEVQYIREAERCYFYVLLELPVTR 1150
1151 RSSKKC 1156
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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