 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P51252 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MVQRISLKNKLLPDLVEIQRDSFKWFLLEGLTEVLEFFPNISDPTSRLEL 50
51 QLFGKEYKIKFPRYSVRQAKSRDRTYSAQIYVPAKLTRKDIDLPSKDQNK 100
101 TIKSLDLSSNHLQFSAEKQIKNKKYKKRLVFIGDLPIMTNRGTFIVSGTE 150
151 RVIINQIIRSPGIYYKQDIDKNGKQIYSASLISNRGSWLKFEIDPKGEIW 200
201 IRIDKTHKVNAYIFLRAIGLNKNEIQKGLSKYAFLISASQSYSVKELAKE 250
251 IGKNDIEEVTDEEALLIVYSKLRPNEPATVPVAKQMLYSRFFDPKRYDLG 300
301 EVGRYKINKKLGLNIPKTFRVLSPQDILSSIDYLINIKDKNSGNLDDIDH 350
351 LGNRRVRSVGELLQNQFRVGLNRLERIIRERMMICDIDSLSLSNLINPKP 400
401 LIASVREFFGSSQLSQFMDQTNPVAELTHKRRISALGPGGFNKDRAGFAV 450
451 RDLHPSHYGRICPIETPEGPNAGLIGSLATCARVNIFGFIETPFYPVHNG 500
501 QVDYSNNPIYLTADEEDDFRVAPGDVKVNVQNYIEGDIIPVRYRQEFVTT 550
551 IPNQVDYIAISPIQVISAATSLIPFLEHDDANRALMGSNMQRQAVPLLYP 600
601 EKPIIGTGLETKIARDSGMVVISRTSGCVNYVSANKIGIQDNNGRTVLYR 650
651 LKKYYRSNQDTCINQRPIVWVGEKIVVGQTLADGASTDCGEIALGRNILV 700
701 AYMPWEGYNYEDAFLISERLVYEDVYTSIHIEKYEVECRQTKLGPEEITR 750
751 EIPNVSDHSLKDLDRNGIVVCGSWVEAGDILVGKITPKGEADQLPEGKLL 800
801 RAIFGEKARDVRDTSLRLPNAAKGRVVNVRVFTRQKGDELPPGTNAMIRV 850
851 YVAQKRKIQVGDKMAGRHGNKGIISRILPKQDMPYLCDGTPVDIVLNPLG 900
901 VPSRMNVGQVFECLLGLAGGYLDKRFKIIPFDEMYGAEASRALVNRKLQE 950
951 ASILTKNKWIFNDQHPGKMQVFDGRTGEPFDNPVTIGRAYMLKLVHLVDD 1000
1001 KIHARSTGPYSLVTQQPLGGRAQHGGQRLGEMEVWALEAFGAAYTLQELL 1050
1051 TVKSDDMQARNEALNAIVKGKPIPKPGTPESFKVLMRELQSLGLDIAVHK 1100
1101 LKLFENGQRRTVEVDLMSDSKEDRVARSNYEVLPVDDFEQFLY 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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