 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NSY1 from www.uniprot.org...
The NucPred score for your sequence is 0.79 (see score help below)
1 MKKFSRMPKSEGGSGGGAAGGGAGGAGAGAGCGSGGSSVGVRVFAVGRHQ 50
51 VTLEESLAEGGFSTVFLVRTHGGIRCALKRMYVNNMPDLNVCKREITIMK 100
101 ELSGHKNIVGYLDCAVNSISDNVWEVLILMEYCRAGQVVNQMNKKLQTGF 150
151 TEPEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLNDGGNYVLCDFG 200
201 SATNKFLNPQKDGVNVVEEEIKKYTTLSYRAPEMINLYGGKPITTKADIW 250
251 ALGCLLYKLCFFTLPFGESQVAICDGNFTIPDNSRYSRNIHCLIRFMLEP 300
301 DPEHRPDIFQVSYFAFKFAKKDCPVSNINNSSIPSALPEPMTASEAAARK 350
351 SQIKARITDTIGPTETSIAPRQRPKANSATTATPSVLTIQSSATPVKVLA 400
401 PGEFGNHRPKGALRPGNGPEILLGQGPPQQPPQQHRVLQQLQQGDWRLQQ 450
451 LHLQHRHPHQQQQQQQQQQQQQQQQQQQQQQQQQQQHHHHHHHHLLQDAY 500
501 MQQYQHATQQQQMLQQQFLMHSVYQPQPSASQYPTMMPQYQQAFFQQQML 550
551 AQHQPSQQQASPEYLTSPQEFSPALVSYTSSLPAQVGTIMDSSYSANRSV 600
601 ADKEAIANFTNQKNISNPPDMSGWNPFGEDNFSKLTEEELLDREFDLLRS 650
651 NRLEERASSDKNVDSLSAPHNHPPEDPFGSVPFISHSGSPEKKAEHSSIN 700
701 QENGTANPIKNGKTSPASKDQRTGKKTSVQGQVQKGNDESESDFESDPPS 750
751 PKSSEEEEQDDEEVLQGEQGDFNDDDTEPENLGHRPLLMDSEDEEEEEKH 800
801 SSDSDYEQAKAKYSDMSSVYRDRSGSGPTQDLNTILLTSAQLSSDVAVET 850
851 PKQEFDVFGAVPFFAVRAQQPQQEKNEKNLPQHRFPAAGLEQEEFDVFTK 900
901 APFSKKVNVQECHAVGPEAHTIPGYPKSVDVFGSTPFQPFLTSTSKSESN 950
951 EDLFGLVPFDEITGSQQQKVKQRSLQKLSSRQRRTKQDMSKSNGKRHHGT 1000
1001 PTSTKKTLKPTYRTPERARRHKKVGRRDSQSSNEFLTISDSKENISVALT 1050
1051 DGKDRGNVLQPEESLLDPFGAKPFHSPDLSWHPPHQGLSDIRADHNTVLP 1100
1101 GRPRQNSLHGSFHSADVLKMDDFGAVPFTELVVQSITPHQSQQSQPVELD 1150
1151 PFGAAPFPSKQ 1161
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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