 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P29294 from www.uniprot.org...
The NucPred score for your sequence is 0.35 (see score help below)
1 MDFRANLQRQVKPKTVSEEERKVHSPQQVDFRSVLAKKGTPKTPVPEKAP 50
51 PPKPATPDFRSVLGSKKKLPAENGSSNAEALNVKATESPKLVGNAPLSGS 100
101 LKPVANAKPAETLKPVANTKPAETLKPVANAETLKPMGNAKPAESSKPVG 150
151 NTKPAETLKPVGNTKPAETLKPVGNIKPAETLKPVGNIKPAETLKPVGNT 200
201 KPTETLKPVANAKSAETLKPIANTKPAETLKPVGNAKPAETLKPVGNAKP 250
251 AETLKPVGNAKPAETLKPVGNAKPAETLKAVANAKPAETPKPAGKEELKK 300
301 EVQNDVNCKREKAGAADNEKPPASPGTAPTFKEKLQDVRVAEGEKLLLQC 350
351 QVSSEPPATITWTLNGKTLKTTKFVILSQEGSLCSVSIEKALPEDRGLYK 400
401 CVAKNAAPEAECSCHVTVHDAPASENAKAPEMKSRRPKSSLPPVLGTESD 450
451 ATVKKKPAPKTPPKAATPPQIPQFPEDQKVRAGERVELFGKVAGTQPITC 500
501 TWMKFRKQIQDSEHIKVENSEAGSKLTILAARQEHCGCYTLLVENKLGSR 550
551 QAQVNLTVVDKPDPPAGTPCASDIRSSSLTLSWYGSSYDGGSAVQSYSVE 600
601 IWDSVDKMWTELATCRSTSFNVRDLLPDREYKFRVRAINVYGTSEPSQES 650
651 ELTTVGEKPEEPKDEVEEVSDDDEKEPEVDYRTVTVNTEQKVSDFYDIEE 700
701 RLGSGKFGQVFRLVEKKTGKIWAGKFFKAYSAKEKENIPAEIGIMNCLHH 750
751 PKLVQCVDAFEEKANIVMVLEIVSGGELFERIIDEDFELTERECIKYMRQ 800
801 ISEGVEYIHKQGIVHLDLKPENIMCVNKTGTRIKLIDFGLARRLENAGSL 850
851 KVLFGTPEFVAPEVINYEPISYATDMWSIGVICYILVSGLSPFMGDNDNE 900
901 TLANVTSATWDFDDEAFDEISDDAKDFISNLLKKDMKNRLDCTQCLQHPW 950
951 LMKDTKNMEAKKLSKDRMKKYMARRKWQKTGNAVRAIGRLSSMAMISGLS 1000
1001 GRKSSTGSPTSPLTAERLETEEDVSQAFLEAVAEEKPHVKPYFSKTIRDL 1050
1051 EVVEGSAARFDCKIEGYPDPEVVWFKDDQSIRESRHFQIDYDEDGNCSLI 1100
1101 ISDVCGDDDAKYTCKAVNSLGEATCTAELIVETMEEGEGEGEEEEEE 1147
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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