 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q3MJ13 from www.uniprot.org...
The NucPred score for your sequence is 0.37 (see score help below)
1 MRTSLQAVALWGQKAPPHSITAIMITDDQRTIVTGSQEGQLCLWNLSHEL 50
51 KISAKELLFGHSASVTCLARARDFSKQPYIVSAAENGEMCVWNVTNGQCM 100
101 EKATLPYRHTAICYYHCSFRMTGEGWLLCCGEYQDVLIIDAKTLAVVHSF 150
151 RSSQFPDWINCMCIVHSMRIQEDSLLVVSVAGELKVWDLSSSINSIQEKQ 200
201 DVYEKESKFLESLNCQTIRFCTYTERLLLVVFSKCWKVYDYCDFSLLLTE 250
251 VSRNGQFFAGGEVIAAHRILIWTEDGHSYIYQLLNSGLSKSIYPADGRVL 300
301 KETIYPHLLCSTSVQENKEQSRPFVMGYMNERKEPFYKVLFSGEVSGRIT 350
351 LWHIPDVPVSKFDGSPREIPVTATWTLQDNFDKHDTMSQSIIDYFSGLKD 400
401 GAGTAVVTSSEYIPSLDKLICGCEDGTIIITQALNAAKARLLEGGSLVKD 450
451 SPPHKVLKGHHQSVTSLLYPHGLSSKLDQSWMLSGDLDSCVILWDIFTEE 500
501 ILHKFFLEAGPVTSLLMSPEKFKLRGEQIICCVCGDHSVALLHLEGKSCL 550
551 LHARKHLFPVRMIKWHPVENFLIVGCADDSVYIWEIETGTLERHETGERA 600
601 RIILNCCDDSQLVKSVLPIASETLKHKSIEQRSSSPYQLGPLPCPGLQVE 650
651 SSCKVTDAKFCPRPFNVLPVKTKWSNVGFHILLFDLENLVELLLPTPLSD 700
701 VDSSSSFYGGEVLRRAKSTVEKKTLTLRKSKTACGPLSAEALAKPITESL 750
751 AQGDNTIKFSEENDGIKRQKKMKISKKMQPKPSRKVDASLTIDTAKLFLS 800
801 CLLPWGVDKDLDYLCIKHLNILKLQGPISLGISLNEDNFSLMLPGWDLCN 850
851 SGMIKDYSGVNLFSRKVLDLSDKYTATLPNQVGIPRGLENNCDSLRESDT 900
901 IVYLLSRLFLVNKLVNMPLELACRVGSSFRMESIHNKMRGAGNDILNMSS 950
951 FYSCLRNGKNESHVPEADLSLLKLISCWRDQSVQVTEAIQAVLLAEVQQH 1000
1001 MKSLGKIPVNSQPVSMAENGNCEMKQMLPKLEWTEELELQCVRNTLPLQT 1050
1051 PVSPVKHDSNSNSANFQDVEDMPDRCALEESESPGEPRHHSWIAKVCPCK 1100
1101 VS 1102
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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