 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P6L8 from www.uniprot.org...
The NucPred score for your sequence is 0.56 (see score help below)
1 MSNRDSANPPGFNDAVPLAICYIDSSTSSHDEYLTLLQSKVSAPDKNFRD 50
51 ETVDGAPFLAGDAISKGHRYSPPVRSDRASAQGLLQQHLEKSSTLENLKR 100
101 YSSTLLGRQNDAVIDEPFAPFRPPPRVTLSESRRDRWLQGLSDPMVPLSS 150
151 LIKTIPHGLWGEDILRMLVKFRVPFTRAIWFIRCAGVNEARSFLRKVQTT 200
201 DITEWVKNWTDVAAGFLISFISSFLNADIYSFADDYTYLLKLFGRLLAEE 250
251 LVSPKHFLLRIVSFSGDSSLKSFSLHFFALQFFSTSLIQYTHICRKCVIT 300
301 ILQSYQQLIVDQPANLLKFSLLSKKVSHFLFTLAQKNIESFFFPTEWDKL 350
351 KPTIILLWKDFPNYSTLLSIMQERNSKAMYMYKPVTSSIRFLQIISCLSF 400
401 PVGWRTLAKDLFKLLPVYTGVPLLLHWCINCRSIFSGDRNFIVSSIFDNA 450
451 NFDRNLIVDLTLSFVLKLHPLEYNECVAAAQLLDHLAACGYFFFSKYIAR 500
501 LASLGYLRESMLNSSFMDDQRKILVQLPILRMSQQLKNKIYYILSKGNYF 550
551 VDWSICDEYVKRFKEDHFSFMFKKEENYAIITLSLVKIASTPMSKLYEDY 600
601 LVMLFAFHYSMFQVMTKLIADNLVHFSFQSCACQTLFFICSVVPKTESQK 650
651 LLLNEMGKLFQVELNFSYDSPDVNLLIEQFYEITSYESNYDDAFVEYKDA 700
701 TVANRKDFIEFLFHNITVSSKHTAVIFTSDLLMVLKIALNHPPYFDDLAT 750
751 TTFSSLLKRDECTILSFFKILQFYGCKLLSVDQIWAVVSDVYEAQDNNTT 800
801 LKQFFNYLLDESTWPEGYLEERHWRSILCKEARKHDSGLKLFKLGIKLCT 850
851 RNEQIMKTIWSFIHVHDCNVISEVIPDQRFLRTLTEHFMIDLRQLDIVTC 900
901 LKKALVTLDEFSAPLYATWLTTLDDDELSELTDDVVQKKVLESLDNYKSG 950
951 IWKLVLSGLPNCKTVFEHLLLFSLEERLDLPAAFLQDLIGASAYVMEQVP 1000
1001 DSWFLEKLPCPLTQSLQSFSHLSNHIEVLDSTRQSRLTFLCHLILHMHGF 1050
1051 VELTDQLATLESLTIRKCIYRNQELLDLLLFSIHLVKPNVETNDEVCNTL 1100
1101 KAWENIESRPYTIDFPEALQQYSPRIVLYEPTFW 1134
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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