 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Z1A9 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MWLKPEEVLLKNALKLWVTQKSSCYFVLQRRRGHGEGGGRLTGRLVGALD 50
51 AVLDSSARVAPFRILLQVPGSQVYSPIACGATLEEINRHWDWLEQNLLHT 100
101 LSVFDNKDDIASFVKGKVKALIAEETSSRLAEQEEEPEKFREALVKFEAR 150
151 FNFPEAEKLVTYYSCCCWKGRVPRQGWLYLSINHLCFYSFFLGKELKLVI 200
201 PWVDIQKLERTSNVFLTDTIRITTQNKERDFSTFLNLDEVFKIMEQLADV 250
251 TLRRLLDNEVFDLDPDLQEPSQITKRDLEARAQNEFFRAFFRLPREEKLH 300
301 AVADCSLWTPFSRCHTAGRIFSSDSYICFASREDGCCNVVLPLREVVSIE 350
351 KMEDTSLLPNPIIVSIRSKMAFQFIELKDRENLVEGLLLRLKQVHANHPV 400
401 HYETSPSDDDMASPVFYSASICTDKFGDLEMVASQSSEEREEKRPLPHPE 450
451 PLTAVFQQSGSQSPDSRLSREQIKISLWNDHFVEYGRTVCMFRTEKIRKL 500
501 VAMGIPESLRGRLWLLFSDAVTDLASHPGYYGNLVEQSLGRCCLVTEEIE 550
551 RDLHRSLPEHPAFQNETGIAALRRVLTAYAHRNPKIGYCQSMNILTSVLL 600
601 LYAKEEEAFWLLVAVCERMLPDYFNHRVIGAQVDQSVFEELIKEQLPELA 650
651 EHMSDLSALASISLSWFLTLFLSIMPLESAVHVVDCFFYDGIKAIFQLGL 700
701 AVLEANAEELCSSKDDGQALMVLSRFLDHIKNEDSPGPPIGSHHAFFSDD 750
751 QEPYPVTDIADLIRDSYEKFGNQSVEQIEHLRCKHRIRVLQGHEDTTKQN 800
801 VLRVVIPEVSILPEDLEELYDLFKRAHMMSCYWEHHRPMALRHDPSRPYA 850
851 EQYRIDARQFAHLFQLVSPWTCGVHTEILAERLFRLLDDNMDQLIEFKAF 900
901 TSCLDIMYNGEMNEKIKLLYRLHIPPALTENDRDSQSPLKNPLLSTSRPL 950
951 VLGKPNGDTIDYQKQLKQMIKDLAKEKDKMEKELPKMSQREFIQFCKTLY 1000
1001 SMFHEDPEENDLYQAIATVTTLLLQIGEVGQRGSSSGSCSQECEEPQASA 1050
1051 PPEQDSVFAEAGKSPQAFPETEGDWTVSLEHILASLLTEQSLVNFFEKPL 1100
1101 NIKSKLENAKLNQYSLKVLEMSHPPQAELKLNDL 1134
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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