 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q1XDN5 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MVQRISLKNKLLPDLVEIQRASFKWFLLEGLTEVLEIFPKISDPTSRLEL 50
51 QLFGNEYKIKFPRYSVRQAKNRDKTYSAQIYVPAKLTRKDIDLPSKNQEK 100
101 NIKSLDLSSTHLQLSAEKQIKNKKYKKRLVFIGDLPIMTNRGTFIVSGTE 150
151 RVIINQIIRSPGIYYKQEIDKNGKQIYSASLISNRGSWLKFEIDPKGEIW 200
201 IRIDKTHKVNAYIFLRAIGLNKDEIEKGLSKYAFLISASQIYSVKELAKE 250
251 IGKNNIEEVTDEEALLIVYSKLRPNEPATVAVAKQMLYSRFFDPKRYDLG 300
301 EVGRYKINKKLGLNIPKTFRVLSPQDILSSIDYLINIKDKNSGNLDDIDH 350
351 LGNRRVRSVGELLQNQFRVGLNRLERIIRERMMICDIDSLSLSNLINPKP 400
401 LIASVREFFGSSQLSQFMDQTNPVAELTHKRRISALGPGGFNKDRAGFAV 450
451 RDLHPSHYGRICPIETPEGPNAGLIGSLATCARVNVFGFIETPFYPVNQG 500
501 QVIYHNSPVYLTADEEDDFRVAPGDVKVSKQHYIEGDIIPVRYRQEFITT 550
551 TPTQVDYIAISPIQVISAATSLIPFLEHDDANRALMGSNMQRQAVPLLYP 600
601 EKPIIGTGLETKIARDSGMVVISRTSGHVNYVSANKIGIQDNSGRTVHYR 650
651 LKKYYRSNQDTCINQRPIVWVGEKIVVGQTLADGASTDGGEIALGRNILV 700
701 AYMPWEGYNYEDAFLISERLVYDDLYTSIHIEKYEVECRQTKLGPEEITR 750
751 EIPNVSDNSLKDLDRNGIVVGGSWVEAGDILVGKITPKGEADQLPEGKLL 800
801 RAIFGEKARDVRDTSLRLPNAAKGRVVKVRVFTRQKGDELPPGTNAMIRV 850
851 YVAQKRKIQVGDKMAGRHGNKGIISRILPKQDMPYLSDGTPVDIVLNPLG 900
901 VPSRMNVGQVFECLLGLAGGYLGKRFKIIPFDEMYGAEASRALVNRKLKE 950
951 ASLITSNKWLFNDQHPGKMQVFDGRTGEPFDNPVTVGRAYMLKLVHLVDD 1000
1001 KIHARSTGPYSLVTQQPLGGRAQHGGQRLGEMEVWALEAFGAAYTLQELL 1050
1051 TVKSDDMQARNEALNAIVKGKPIPKPGTPESFKVLMRELQSLGLDIAVHK 1100
1101 LKLFEDGQRRTVEVDLMSDSKDNRVDRSNYDTPPVDDFEQFLY 1143
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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