 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q09564 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MNNEQLQSTSSSPSTLLKPIEKKSRFLRLYSSDGQRSTRIECDEHTTCAQ 50
51 ICQQFSCDVITLQIGNYHFRQLKSDTSPLAILQDFCRILTMSDENLETES 100
101 TSKALEDILSYTDSPAFRHIFSFFLGRPRIRAKSTLNLDILSEEVVIRKG 150
151 KLIHTWQNARAVFYNSNIRLQTKNNQDEALQIRNMKVDVHESKRGRALRL 200
201 KDDDVCYLIIFQRPNILEAWLTRAQQVEKSNHVDASDEQLTLIPEQILNN 250
251 EARVQILNLRRNSLISRPPTEKSMAPLGYIDDLYRVHSLQVIDLSANQIL 300
301 SFPIQLTLLSHLRQLNLSSNYISSVPSECSNMRRLQYLNLSNNQLDTLPD 350
351 SISELQNLQSLDISFNQFSQIPPCLFHLTLEMWRLAGNNIEKIDRVGEMQ 400
401 IQKIDLRRNVLDTSFRLDIENITHLDLRDNSMISTVHLTNLRFLKVIHCE 450
451 RLQLSSLHLSGESLTHVYADHNLLDSLVVMPLPQNLQTLSLSFNHFRNLP 500
501 DWISDCPNLTFLRANNNSLVALPERIFYSQSLRSIFAFINEIEHIPDFGE 550
551 ENCLETLILYKNKLSSLPKHFFSILPRLRQLNISSNFIELLPYFDGSSFC 600
601 RLQILRCANNYLTENSVPVIVNMKHLKIIDLSHNRLNSFDDSALSSLELL 650
651 EDLNLSSNRLTRLADCLALLPCLQVLRAHSNQLVHVPEFRASNQLHTIDV 700
701 SSNNISLGTLQFKAPPNLRHFDVTCNSGDFDTENFPENAKMHSKMNTINI 750
751 SEGSQNLFGFQIGVSGSRGMKNKQCIRQVRVENTFGFIDGGSNSYMSSSI 800
801 CRFMTSYLKENVSMDIRSILLRCHCELGEEGERLGASVMIIRVHEKRLEI 850
851 ASTGTMSAAIARNQKLKMIINGRYEIDDDEYSRIREAHGFVDEENRINGV 900
901 IGSSRQIGHFSTFPVVLPTHSYRNIQLNEQIEGLIVGNNMIWNMLSIDDL 950
951 NSTFHNNRSPIVVAKKIQDQLQSYDYGGNSNILVLRRIKPQMTFNGFSTS 1000
1001 STKNQVTPDIAIPKIDEQLVLAVPALILPEYHPSPPVPPPIPAIRHRTPS 1050
1051 PPPPPLPSSTPPPVSSEHEEINVRQSSTLSGGYTLSIDRFYSSSSTVSSR 1100
1101 KQFNETRDLLSKSLKLSPPNTVTFNI 1126
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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