 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P33539 from www.uniprot.org...
The NucPred score for your sequence is 0.46 (see score help below)
1 NSIVRKRIEISTNENRGQLVVSSDKYSLVDNLFYNLHDYASQSKHNIIID 50
51 DGKVEKLLNLICNVLDRNVNELNDSVFLIIKDIEKECKYYVSNVLYRSVG 100
101 SRKNRGREVEWSSYKYNKEFNKVLDKGIISINNEVLKFISKEREGYIERV 150
151 ESIAVTVKNKILELNNNIAEVLLSIKNKVIVLNKESVVAKVEEINYEVHN 200
201 KFIKGNGNTNFSNRNLTEIKSILKELNKMEILDNRINKLSTKESDLLKVI 250
251 KEILDSNLIIEDKQLAIEKTVVEYELTFFRHNMDTHETRNKIIHNIYPKL 300
301 NKAYTELLANYKLNRYSKIKKSIHLISNKSEGTKSKEMIKLIVVLVILYI 350
351 GIDKCISYSFYQIINLLTNARDGTSRTNIAINLGFRIIKVLKYIKLDENP 400
401 SLNALYPINKLKDEISKLDNEGIYWIGDTLLGLITANCDIVVEELKWNSG 450
451 KDSQLEVRINDKFISNLTVSGINIVQLPMLTEPRKISSDGLYFPYINSDT 500
501 TNLHLFEGELIKGKYNLRDHTEASEMLYSSINYLNSIKFKINKAMLNFIL 550
551 AEWDNKDSKLFKGYNMLKPILETDSKEIKEEKVSSNSKYTLYSNIISLAS 600
601 LYKDNEFYLPVYVDFRGRVYPLSNYISYQGGDLARSLILFADTKCVLNNS 650
651 GKECLNVYLANLAGYDKLPWSERLTKVDGIIKEYLESNEISNTKYIEDNI 700
701 DKISEPFQFISIMYAKLLSISNPKANISNPILFDASCSGIQHIAALTLEK 750
751 ELASNVNLYTDSSNPKEDYPQDFYTYALEKIRDKLINSDITELRDIKLNR 800
801 KIIKRSVMTIPYNISMAGIGEHLMEHFTVKTVLKYRYVVIPGSATISSKD 850
851 VYLDYSKYGQLCKIIYFVLTKELPSLRLLSNYFESMIDIFVKLNIPITWV 900
901 TPSGLKIKYTNIKFKPQKVKTSVLNTSKITTIKLPTDSLDVLSTKRSFMP 950
951 NFIHSLDASNVHLLLNSVSYKNLPVYTVHDCFASTANNMFKLEKLVKNAF 1000
1001 INIYFNDEGYLLKLHKHFVDTIISATDPYLSNGNIENENDIKGLTTERLE 1050
1051 YKPLLSSNYVADRISTKADIIKIPDLPAGYKNKNKNINEFVKGILNSKYF 1100
1101 IG 1102
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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