 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O74755 from www.uniprot.org...
The NucPred score for your sequence is 0.75 (see score help below)
1 MDVMNVPVDSERDNPGDKVETQSDKNHLPKASPSQSQSPVNTSLHNGDGK 50
51 DNGVATEPVENKQILSERSVTRDDYEKGKTIVSSLALSSISGKDGSISSQ 100
101 NAEGLSSSSNRPLDVNDALSYLELVKYYFSERREIYNRFLEIMRDFKSQA 150
151 LDTLGVINRVSELFNGYPQLIEGFNTFLPSGYKIEVQLDSSNTSVVRVGT 200
201 PMHPLPQQGVQSTLPVAPSNEDQRTMESTSPTDSQPQPSAPNLVSSTENE 250
251 KPRVDFNYAIAYMNKVKARYPPNSDTYMEFLGVLRTYQKAQKSIFEVRAR 300
301 VAEIFKDSPDLLEEFKLFLPDNVDSTEPSTPNVQKSPNRLPPVGNFSLPP 350
351 SAPVREKRNRPAHSAQISRSISKTSRMYRQTAEEPLNSYSLHVYPQKITA 400
401 PTSPYAATQEELLAFTTIRQHLPDTLAFHKFLELLHLYREKLLDKTELLN 450
451 SFSKLVRNDNLTLWFSEFIRWSDNPILVKNEPVDERVYLPETFECISLTY 500
501 RKLPDSWKQDKCSGRDDLDNSVLNDDYISVAPKPSHVKNIMHHENQYLQA 550
551 LQLVEDERYDYDRVLNTTESAIKILANFCEPTIHEHLETALQELERSKRI 600
601 IKNALIIVYGKEHANLALDTLFKKLPTAAPVLLKRIKTKDQEWRRSKREW 650
651 SKIWRQIEKKNAQAAFDDRYCRIEGRDRRGLSYSRILRDIDDIYQRQKHR 700
701 IDGAKLGFQFTQVLCDSLIFLNILRLSDAQLTNSSFYSYADKGRISAVLK 750
751 ALLSQFFGIPLPREALETNLASENIESVKKHRDGLSKIFIRPESADNSNN 800
801 TNVSFQTDETQTEDETMSDIHPDDVENHSKSKFLGEESKNIIGYNFFGNA 850
851 TMYVLFRLICVCYSRLEHIKLFVESSTIYASSTGGYENILNICEKYLKGS 900
901 CSRLEFRKYLQKFNNETCYMICSIERLLKVIFYRIHEILLDPKLGQLLLL 950
951 FESDGANSVTTPREQMVYRNHVESILAPESKIFNMRWYPLEKRLCIQQLL 1000
1001 PADLTMHDFENPAKAFMYYVDSYAISHITEGVDLMQVKMPFLRRSLQRIS 1050
1051 QQGYLAGRGSGRLHSLFNEHFCKSNLQLFFSTDTYVIFFEPNTENVYINS 1100
1101 YNLWVDQSSQSKKQNRTTNWRRWLESDEGWRKSKANTDIKFFSETTLDQC 1150
1151 IEAM 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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