 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P48231 from www.uniprot.org...
The NucPred score for your sequence is 0.23 (see score help below)
1 MSPNSSKTRTDQISSMPGINEATKVESKNVVKDAVPIKSEVETNGTSIVR 50
51 EKQDPSYVGWKQVGGWEEKDELTSEDLLVDVNKDTFLGNLLPDKFYGDWY 100
101 HEVAILIIAGLCSFVLGYFKFSLASVLIVMLTTGMLYRTSSKKYRESLRD 150
151 LAQKEQTVEKITSDYESVEWLNTFLDKYWPIIEPSVSQQIVDGTNTALSE 200
201 NVAIPKFIKAIWLDQFTLGVKPPRIDAIKTFQNTKSDVVVMDVCLSFTPH 250
251 DMSDLDAKQCRNYVNSNVVLKAKIFGMDIPVSVADIFFQVFVRFRFQLMT 300
301 TLPLVETINIQLLEVPEVDFISRLLGNSVFNWEILAIPGLMRLIQKMAFK 350
351 YLSPVLLPPFSLQLNIPQLLSKTGLPIGVLEIKVKNAHGLRKLVGMIKKT 400
401 VDPYLTFELSGKIVGKTKVFKNSANPVWNESIYILLQSFTDPLTIAVYDK 450
451 RETLSDKKMGTVIFNLNKLHANHYHKNEKVHFLRNSKPVGELTFDLRFFP 500
501 TIEPKKLLNGDEEPLPDMNTGITKITIRELKGLDELSDKKFVFAELYVNA 550
551 ELVMTTKKEKRTAHLKWNSDYYSVVTDRRKTICRFVLKDQSGKVISSSVQ 600
601 PLNHLIDRTEVNKEWIPLRNGKGELKVTTYWRPVDIDLGLKSVGYTTPIG 650
651 MLRVFINKAENLRNPDSLGKISPYAKVSVNGVARGRTNERIETLNPIWNQ 700
701 SIYVSVTSPLQKVSIDCFGIDTNGDDHNLGSLNIQTQNIYHKDNDDKYTI 750
751 FIDNAPRTGNLIGKKGVKGTVTYYLSFYPVVPVLSLEEAKEVDEINEKKD 800
801 KLEKQKSTLDDKNISKEEKERIKKEEFRLTEKYDMYSYKMKLDLDELLQY 850
851 NAGVLGVTVLGGELPQPGLYVQTFFDSCGYAAITSAKNAIRTIKTGWSGD 900
901 FMIKELEWSVTTFRVTKTKDANKAENFICEVNIPTIELVRNCYYKPSVLN 950
951 LIGKKSAKLLVQVSWFPVTATELPQSDLITNSGDLKITAKSAENLIGVNK 1000
1001 NGYSDPYVEFFLNEKSTSPFFKTAVQKKTLNPTWNESKTIEVSNRVNDYL 1050
1051 TINVKDYESTNSNRSIGKAVVPLSTIDPESDTTFNIPLVGPKGEDGGVLH 1100
1101 LEFEFEPRYTTNVVKREAGLGNFATKGLGTGIKAGSTVFALGTNVVSTGL 1150
1151 GTIDKVKAGVFGGKKSTTTGDKKSEEKQ 1178
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.) |
Go back to the NucPred Home Page.