 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9R1M7 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MRRLSLWWLLSRVCLLLPPPCALVLAGVPSSSSHPQPCQILKRIGHAVRV 50
51 GAVHLQPWTTAPRAASRAQEGGRAGAQRDDPESGTWRPPAPSQGARWLGS 100
101 ALHGRGPPGSRKLGEGAGAETLWPRDALLFAVENLNRVEGLLPYNLSLEV 150
151 VMAIEAGLGDLPLMPFSSPSSPWSSDPFSFLQSVCHTVVVQGVSALLAFP 200
201 QSQGEMMELDLVSSVLHIPVLSIVRHEFPRESQNPLHLQLSLENSLSSDA 250
251 DVTVSILTMNNWYNFSLLLCQEDWNITDFLLLTENNSKFHLESVINITAN 300
301 LSSTKDLLSFLQVQMDNIRNSTPTMVMFGCDMDSIRQIFEMSTQFGLSPP 350
351 ELHWVLGDSQNVEELRTEGLPLGLIAHGKTTQSVFEYYVQDAMELVARAV 400
401 ATATMIQPELALLPSTMNCMDVKTTNLTSGQYLSRFLANTTFRGLSGSIK 450
451 VKGSTIISSENNFFIWNLQHDPMGKPMWTRLGSWQGGRIVMDSGIWPEQA 500
501 QRHKTHFQHPNKLHLRVVTLIEHPFVFTREVDDEGLCPAGQLCLDPMTND 550
551 SSMLDRLFSSLHSSNDTVPIKFKKCCYGYCIDLLEQLAEDMNFDFDLYIV 600
601 GDGKYGAWKNGHWTGLVGDLLSGTANMAVTSFSINTARSQVIDFTSPFFS 650
651 TSLGILVRTRDTAAPIGAFMWPLHWTMWLGIFVALHITAIFLTLYEWKSP 700
701 FGMTPKGRNRNKVFSFSSALNVCYALLFGRTAAIKPPKCWTGRFLMNLWA 750
751 IFCMFCLSTYTANLAAVMVGEKIYEELSGIHDPKLHHPSQGFRFGTVRES 800
801 SAEDYVRQSFPEMHEYMRRYNVPATPDGVQYLKNDPEKLDAFIMDKALLD 850
851 YEVSIDADCKLLTVGKPFAIEGYGIGLPPNSPLTSNISELISQYKSHGFM 900
901 DVLHDKWYKVVPCGKRSFAVTETLQMGIKHFSGLFVLLCIGFGLSILTTI 950
951 GEHIVHRLLLPRIKNKSKLQYWLHTSQRFHRALNTSFVEEKQPRSKTKRV 1000
1001 EKSRWRRWTCKTEGDSELSLFPRSNLGPQQLMVWNTSNLSHDNQRKYIFN 1050
1051 DEEGQNQLGTQAHQDIPLPQRRRELPASLTTNGKADSLNVTRSSVIQELS 1100
1101 ELEKQIQVIRQELQLAVSRKTELEEYQKTNRTCES 1135
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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