 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P93736 from www.uniprot.org...
The NucPred score for your sequence is 0.37 (see score help below)
1 MSLLFLRRAKPLFVSCCSATHSRSSFLSPTLTNQLVRSFHGSRTMSESEK 50
51 KILTEEELERKKKKEEKAKEKELKKQKALEKERLAELKAKQAKDGTNVPK 100
101 KSAKKSSKRDASEENPEDFVDPETPLGERKRLSSQMAKQYSPATVEKSWY 150
151 AWWEKSDLFKADAKSSKPPFVIVLPPPNVTGALHIGHALTSAIEDTIIRW 200
201 KRMSGYNALWVPGVDHAGIATQVVVEKKIMRDRGMTRHDVGREEFVKEVW 250
251 KWKNQYGGTILTQLRRLGASLDWSRECFTMDEQRSKAVTEAFVRLYKEGL 300
301 IYRDIRLVNWDCILRTAISDVEVEYIDIKEKTLLKVPGYEKPVEFGLLTS 350
351 FAYPLEGGLGEVIVATTRVETMLGDTAIAIHPDDARYKHLHGKFAVHPFN 400
401 GRKLPIICDGILVDPNFGTGCVKITPAHDPNDCEVGKRHKLEFINIFTDD 450
451 GKINTNGGSDFAGMPRFAAREAVVEALQKQGLYRGAKNNEMRLGLCSRTN 500
501 DVIEPMIKPQWYVNCSMIGKEALDVAITDENKKLEFVPKQYTAEWRRWLE 550
551 NIRDWCISRQLWWGHRIPAWYATLEEDQLKEVGAYSDHWVVARTEDDARE 600
601 EAAQKFLGKKFELTRDPDVLDTWFSSGLFPLSVLGWPDVTDDFKAFYPTS 650
651 VLETGHDILFFWVARMVMMGMKLGGEVPFSKVYFHPMIRDAHGRKMSKSL 700
701 GNVIDPLEVINGVTLEGLHKRLEEGNLDPKEVIVAKEGQVKDFPNGIPEC 750
751 GTDALRFALVSYTAQSDKINLDILRVVGYRQWCNKLWNAVRFAMMKLGDG 800
801 YTPPQTLSPETMPFSCQWILSVLNKAISKTVVSLDAFEFSDAANTIYAWW 850
851 QYQFCDVYIEAIKPYFAGDNPTFASERAHAQHALWISLETGLRLLHPFMP 900
901 FVTEELWQRLPAPKDTERKASIMICDYPSAIENWSNEKVESEMDTVLATV 950
951 KCMRALRAGLLEKQKNERLPAFALCENNVTSEIVKSHELEIRTLANLSSL 1000
1001 EVVSKGQHAAPPGSSVETVNENLKVYLEVDGAINTEAEQEKIRNKIGELQ 1050
1051 KQKEKLQKMMSVSTYEEKVPANIKEDNANKLAKILQEFDFFEKESARLAA 1100
1101 ETSNSGNQ 1108
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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