 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q4P3W3 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MAKTIDFLASDGEDNFAARTISATSKPNRNDATASSSKPQKRARRSTKGS 50
51 DDDGNDDFDIASSLLASAAEPAAVGMLSSSQQASQKAYASRVSAKDDDDD 100
101 DARFIASVMQHANIKAGLEVAKKALSGKNKGKNKLGSGVVTGGGSFQSMG 150
151 LHPSLLRSLLIRGFTTPTPIQRQAIPAIMSQPPRDVVGMARTGSGKTLAY 200
201 LIPLINRLNGRHSPTFGIKSLILCPSRELAVQILRVGKEIARGWKADAGE 250
251 GQDSRGEAIRWAIIVGGESLDEQFGIMSNNPDVVIATPGRMLHLTVEMNL 300
301 DLKSVEYVVFDEADRLFEMGFAEQLEEMLLRLPPTRQTLLFSATLPKKLV 350
351 EFTKAGLQANPKLVRLDADSKISADLRMAFFSVKPSEKEAALLVLLRDVI 400
401 GVPLGEQAARDLDEEAQFNEDASDNQADGQRSRGYGNRRAEFKGKTKDKH 450
451 LGNKRKRGGPGGALELLPHQTIIFCATKHHVEYLLLLLTTTGYACSHIYS 500
501 SLDQATRGIQMSRFRRGQNSLLIVTDVAARGIDLPVLEHVVNFDFPPQPR 550
551 TFVHRVGRTARAGRNGWAWSMCTNAELPYLCDLQLFLARPLVSSHTAIAA 600
601 LANGRDVASADALGLHDSLILGTLPREALDLETEFISSSLTNTSSSTAHD 650
651 FPALRAVADRAQQKYEKSIAKASQESHRRAKEMVKLGSIEQINIRTASGR 700
701 EGQVPEWTLAGSPLEEMAVHDVVKRPEVYGLNRANKADAVTSALGSDRMD 750
751 DKVKRGTDNALKEADEAAKRAALLAKVNAFRPQETVFEIGIRGDATPLGA 800
801 LMRSRRQTMQVKTKRAEALEARKRAIEGGGDAMEDDEEVTKPKVAARNKG 850
851 KKKATAGKADNDEAVVGDAMVDMEQADEADILAAFDTTKPSKAQVQREAL 900
901 TDSETDASESDRDAASARHTKRARTKKAAPTSYRDPNFYLSYEQQGSTSE 950
951 RGYSLNNARSHTDSFIQQASAVSFDLAGDDATLGTQSQRPNVTRWDSKKK 1000
1001 NFIQATVGADNKKMIRTESGVRLPASFRSGRYEDWKREKRIDMPKTGEIE 1050
1051 SNNHRVHERSPPETSVMGLKRFRHTKLSAPKTAGVFGNRRPGQPKRQAAK 1100
1101 DEVKSARQIQKDRELKEKRREKNARPSKSDTNRRAKRGAARRGRGGHGPR 1150
1151 GASR 1154
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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