 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q62469 from www.uniprot.org...
The NucPred score for your sequence is 0.14 (see score help below)
1 MGPGQAGGALLLRLLMLVQGILNCLAYNVGLPGAKIFSGPSSEQFGYSVQ 50
51 QLTNPQGNWLLVGSPWSGFPENRMGDVYKCPVDLPTATCEKLNLQNSASI 100
101 SNVTEIKTNMSLGLTLTRNPGTGGFLTCGPLWAHQCGNQYYATGICSDVS 150
151 PDFQFLTSFSPAVQACPSLVDVVVVCDESNSIYPWEAVKNFLVKFVTGLD 200
201 IGPKKTQVALIQYANEPRIIFNLNDFETKEDMVQATSETRQHGGDLTNTF 250
251 RAIEFARDYAYSQTSGGRPGATKVMVVVTDGESHDGSKLKTVIQQCNDDE 300
301 ILRFGIAVLGYLNRNALDTKNLIKEIKAIASTPTERYFFNVADEAALLEK 350
351 AGTLGEQIFSIEGTVQGGDNFQMEMAQVGFSADYAPQNDILMLGAVGAFD 400
401 WSGTLVQETSHKPVIFPKQAFDQVLQDRNHSSFLGYSVAAISTEDGVHFV 450
451 AGAPRANYTGQIVLYSVNKQGNVTVIQSHRGDQIGSYFGSVLCSVDVDKD 500
501 TITDVLLVGAPTYMNDLKKEEGKVYLFTITKGILNQHQFLEGPEGTGNAR 550
551 FGSAIAALSDINMDGFNDVIVGSPVENENSGAVYIYNGHQGTIRTKYSQK 600
601 ILGSNGAFRRHLQFFGRSLDGYGDLNGDSITDVSIGALGQVIQLWSQSIA 650
651 DVAIEALFTPDKITLLNKDAKITLKLCFRAEFRPAGQNNQVAILFNMTLD 700
701 ADGHSSRVTSRGVFRENSERFLQKNMVVNEVQKCSEHHISIQKPSDVVNP 750
751 LDLRVDISLENPGTSPALEAYSETVKVFSIPFYKECGSDGICISDLILDV 800
801 QQLPAIQTQSFIVSNQNKRLTFSVILKNRGESAYNTVVLAEFSENLFFAS 850
851 FSMPVDGTEVTCEVGSSQKSVTCDVGYPALKSEQQVTFTINFDFNLQNLQ 900
901 NQAAINFQAFSESQETNKADNSVSLTIPLLYDAELHLTRSTNINFYEISS 950
951 DENAPSVIKSVEDIGPKFIFSLKVTAGSAPVSMALVTIHIPQYTKEKNPL 1000
1001 LYLTGIQTDQAGDISCTAEINPLKLPHTAPSVSFKNENFRHTKELDCRTT 1050
1051 SCSNITCWLKDLHMKAEYFINVTTRVWNRTFAASTFQTVQLTAAAEIDTH 1100
1101 NPQLFVIEENAVTIPLMIMKPTEKAEVPTGVIIGSIIAGILLLLAMTAGL 1150
1151 WKLGFFKRQYKKMGQNPDEMDETTELNS 1178
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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