 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O12990 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MPELAVMDLGRQLCVKMKKQRKAEMTIPTAMKGLEIHFYLADTHQLEFFK 50
51 ACYTAEDLCVEAAKRCRISPLCHNLFALYEESQDLWYAPNHVFKVTDETS 100
101 IKLHYRMRFYFTNWHGTSEIESPVWRHTLSKQKSVLNSQKTTEGTPLLDA 150
151 ASLDYLFAQGQYDFLRGLSPVRPTQTDEEHHEIENECLGMAVLAITHHAK 200
201 SNNLPLSGAGAETSYKRFIPDSLNRTIKQRNFLTRIRISNVFKNFLNEFN 250
251 SKTIQDSNIGLYDLKVKYLSTLETLTQGVGREIFKPKNLKVTGESEGSPA 300
301 QMLPLGDNGMGYEVQVYGTTGISWRRKPAPNQLILKDKPKSKKIKGDKQW 350
351 NDKKKDSGWTLFSDFHEITHIVIKDCCVTIYRQDNKTMELDLFYRDAALS 400
401 FAALVDGYFRLTVDAHHYLCTDVAPSSVVQNLENGCHGPICTEYAIHKLR 450
451 QEGNEEGTYVLRWSCTEYNFIIMTVVCIELDLCESRPVPQYKNFQIETSP 500
501 QGYRLYGTDTFRPTLKELLEHLQGQLLRTDNLRFQLRRCCPPQPREISNL 550
551 LVMTTDREPVPQKKTQVSQLSFDRILKEEIVQGEHLGRGTRTNIYAGILK 600
601 PKSDDEDDLGGYSQEVKVVLKVLGSGHRDISLAFFETASMMRQISHKHTA 650
651 LLYGVCVRHQENIMVEEFVQYGPLDLFMRRQTTPLSTAWKFQVAKQLASA 700
701 LSYLEDKKMVHGYVCSKNILVARDGLDGEGGPFIKLSDPGIPITVLSREE 750
751 CVDRIPWIAPECVKDTANLTIAADKWSFGTTLWEICYNGEIPLKDKKLSE 800
801 KERFYAAQCQLATPDCDELAKLMTHCMTYDPRQRLFFRAIVRDIVMVEKQ 850
851 NPSIQPVPMLEVDPTVFEKRFLKKIRDLGEGHFGKVELCRYDPRGDRTGE 900
901 LVAVKSLKPENREEQSNNLWREIHILRELYHENIVKYKGICNEEGGRSIK 950
951 LIMEFLPAGSLKEYLPRNKAHINLKTLHNYSVQICQGMDYLGSRNYIHRD 1000
1001 LAARNVLVENEGTVKIGDFGLTKSIKDNEGYYTVKDDLDSPVFWYAPECL 1050
1051 IHCKFYRASDVWSFGVTMYELLTYCDASCSPMSVFLKLIGPTHGQMTVTR 1100
1101 LVKVLEEGKRLPRPDDCSEQLYNLMRRCWEATPEKRIDFKSLIANFQQML 1150
1151 DNL 1153
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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