 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P05790 from www.uniprot.org...
The NucPred score for your sequence is 0.16 (see score help below)
1 MRVKTFVILCCALQYVAYTNANINDFDEDYFGSDVTVQSSNTTDEIIRDA 50
51 SGAVIEEQITTKKMQRKNKNHGILGKNEKMIKTFVITTDSDGNESIVEED 100
101 VLMKTLSDGTVAQSYVAADAGAYSQSGPYVSNSGYSTHQGYTSDFSTSAA 150
151 VGAGAGAGAAAGSGAGAGAGYGAASGAGAGAGAGAGAGYGTGAGAGAGAG 200
201 YGAGAGAGAGAGYGAGAGAGAGAGYGAGAGAGAGAGYGAGAGAGAGAGYG 250
251 AGAGAGAGAGYGAASGAGAGAGYGQGVGSGAASGAGAGAGAGSAAGSGAG 300
301 AGAGTGAGAGYGAGAGAGAGAGYGAASGTGAGYGAGAGAGYGGASGAGAG 350
351 AGAGAGAGAGAGYGTGAGYGAGAGAGAGAGAGAGYGAGAGAGYGAGYGVG 400
401 AGAGYGAGYGAGAGSGAASGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAG 450
451 AGSGAGAGSGAGAGSGAGAGSGTGAGSGAGAGYGAGAGAGYGAGAGSGAA 500
501 SGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGAGAGYGAGAG 550
551 AGYGAGAGVGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAG 600
601 SGAGAGSGAGAGSGAGAGSGAGAGSGAGVGYGAGVGAGYGAGYGAGAGAG 650
651 YGAGAGSGAASGAGAGAGAGAGTGSSGFGPYVANGGYSRSDGYEYAWSSD 700
701 FGTGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGVGVGYG 750
751 AGYGAGAGAGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAG 800
801 SGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGVGSGAGAGSGAGAGVG 850
851 YGAGAGVGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSG 900
901 AGAGSGAGAGSGAGAGSGAGAGSGAGVGYGAGVGAGYGAGYGAGAGAGYG 950
951 AGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAG 1000
1001 SGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGAGAGYGAGYGAGAGAG 1050
1051 YGAGAGSGAASGAGSGAGAGSGAGAGAGSGAGAGSGAGAGSGAGAGSGAG 1100
1101 AGSGAGAGSGAGAGYGAGVGAGYGAGYGAGAGAGYGAGAGSGAASGAGAG 1150
1151 SGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGVGYGAGYGAG 1200
1201 AGAGYGAGAGSGAASGAGAGAGAGAGTGSSGFGPYVAHGGYSGYEYAWSS 1250
1251 ESDFGTGSGAGAGSGAGAGSGAGAGSGAGAGSGAGYGAGVGAGYGAGYGA 1300
1301 GAGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGA 1350
1351 GSGAGAGSGAGAGSGAGAGYGAGYGAGAGAGYGAGAGSGAGSGAGAGSGA 1400
1401 GAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGVGAGYGA 1450
1451 GYGAGAGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGVGSGAGAGS 1500
1501 GAGAGSGAGAGSGAGAGYGAGYGAGAGAGYGAGAGSGAGSGAGAGSGAGA 1550
1551 GSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGVGYGAGVGAGYGAGY 1600
1601 GAGAGAGYGAGAGSGAASGAGAGAGAGAGTGSSGFGPYVANGGYSGYEYA 1650
1651 WSSESDFGTGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGYGAGAG 1700
1701 AGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSG 1750
1751 AGAGSGAGSGSGAGAGSGAGAGSGAGAGYGAGVGAGYGVGYGAGAGAGYG 1800
1801 AGAGSGAASGAGAGAGAGAGTGSSGFGPYVAHGGYSGYEYAWSSESDFGT 1850
1851 GSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGVGA 1900
1901 GYGAAYGAGAGAGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGA 1950
1951 GAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGAGAGYGA 2000
2001 GAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGSGSGAGAGS 2050
2051 GAGAGSGAGAGYGAGVGAGYGAGYGAGAGAGYGAGAGSGAGSGAGAGSGA 2100
2101 GAGYGAGAGAGYGAGYGAGAGAGYGAGAGTGAGSGAGAGSGAGAGSGAGA 2150
2151 GSGAGAGSGAGAGSGAGAGSGAGSGSGAGAGSGAGAGSGAGAGSGAGAGS 2200
2201 GAGAGSGAGAGYGAGAGAGYGAGYGAGAGAGYGAGAGSGAGSGAGAGSGA 2250
2251 GAGSGAGAGSGAGAGYGAGYGAGAGSGAASGAGAGAGAGAGTGSSGFGPY 2300
2301 VAHGGYSGYEYAWSSESDFGTGSGAGAGSGAGAGAGAGAGSGAGAGYGAG 2350
2351 VGAGYGAGYGAGAGAGYGAGAGSGTGSGAGAGSGAGAGYGAGVGAGYGAG 2400
2401 AGSGAAFGAGAGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAG 2450
2451 YGAGVGAGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSG 2500
2501 AGAGYGAGVGAGYGAGYGAGAGAGYGAGAGSGAASGAGAGSGAGAGAGSG 2550
2551 AGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGSGAGAGYGAGAGSGAASG 2600
2601 AGAGAGAGAGTGSSGFGPYVANGGYSGYEYAWSSESDFGTGSGAGAGSGA 2650
2651 GAGSGAGAGSGAGAGSGAGAGYGAGVGAGYGAGYGAGAGAGYGAGAGSGA 2700
2701 GSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGAGS 2750
2751 GAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGV 2800
2801 GAGYGVGYGAGAGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGS 2850
2851 GAGSGAGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGYGVGYGAGAGAGY 2900
2901 GAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGSGAGAGS 2950
2951 GAGAGYGAGVGAGYGVGYGAGAGAGYGAGAGSGAGSGAGAGSGAGAGSGA 3000
3001 GAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGSGAGAGS 3050
3051 GAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGVGAGYGVGYGAGV 3100
3101 GAGYGAGAGSGAASGAGAGSGAGAGAGSGAGAGSGAGAGSGAGAGSGAGA 3150
3151 GSGAGAGSGAGAGYGAGYGAGVGAGYGAGAGVGYGAGAGAGYGAGAGSGA 3200
3201 ASGAGAGAGSGAGAGTGAGAGSGAGAGYGAGAGSGAASGAGAGAGAGAGT 3250
3251 GSSGFGPYVANGGYSGYEYAWSSESDFGTGSGAGAGSGAGAGSGAGAGSG 3300
3301 AGAGSGAGAGYGAGVGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAG 3350
3351 AGSGAGAGYGAGAGSGTGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSG 3400
3401 AGAGSGVGAGYGVGYGAGAGAGYGVGYGAGAGAGYGAGAGSGTGSGAGAG 3450
3451 SGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGVGAGYGVGYG 3500
3501 AGAGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGSGAGAG 3550
3551 SGAGAGSGAGAGSGAGSGAGAGSGAGAGYGVGYGAGAGAGYGAGAGSGAG 3600
3601 SGAGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAG 3650
3651 VGAGYGVGYGAGAGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAG 3700
3701 SGAGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAG 3750
3751 VGAGYGVGYGAGAGAGYGAGAGSGAASGAGAGAGAGAGTGSSGFGPYVAN 3800
3801 GGYSGYEYAWSSESDFGTGSGAGAGSGAGAGSGAGAGYGAGYGAGVGAGY 3850
3851 GAGAGVGYGAGAGAGYGAGAGSGAASGAGAGAGAGAGSGAGAGSGAGAGA 3900
3901 GSGAGAGYGAGYGIGVGAGYGAGAGVGYGAGAGAGYGAGAGSGAASGAGA 3950
3951 GSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGYGAGVGAGY 4000
4001 GAGAGVGYGAGAGAGYGAGAGSGAASGAGAGAGAGAGAGSGAGAGSGAGA 4050
4051 GSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGYGAGVGA 4100
4101 GYGAGYGGAGAGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAG 4150
4151 AGSGAGAGYGAGAGSGAASGAGAGAGAGAGTGSSGFGPYVNGGYSGYEYA 4200
4201 WSSESDFGTGSGAGAGSGAGAGSGAGAGYGAGVGAGYGAGYGAGAGAGYG 4250
4251 AGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGSGAGAGSGAGAGSG 4300
4301 AGAGSGAGAGSGAGAGSGAGAGYGAGVGAGYGAGYGAGAGAGYGAGAGSG 4350
4351 AASGAGAGSGAGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAG 4400
4401 SGAGAGSGAGAGYGAGYGAGVGAGYGAGAGVGYGAGAGAGYGAGAGSGAA 4450
4451 SGAGAGSGSGAGSGAGAGSGAGAGSGAGAGAGSGAGAGSGAGAGSGAGAG 4500
4501 YGAGYGAGAGSGAASGAGAGAGAGAGTGSSGFGPYVANGGYSGYEYAWSS 4550
4551 ESDFGTGSGAGAGSGAGAGSGAGAGYGAGVGAGYGAGYGAGAGAGYGAGA 4600
4601 GSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGA 4650
4651 GAGYGAGYGAGAGAGYGAGAGVGYGAGAGAGYGAGAGSGAGSGAGAGSGS 4700
4701 GAGAGSGSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGA 4750
4751 GAGYGAGYGIGVGAGYGAGAGVGYGAGAGAGYGAGAGSGAASGAGAGSGA 4800
4801 GAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGA 4850
4851 GYGAGAGVGYGAGAGSGAASGAGAGSGAGAGSGAGAGSGAGAGSGAGAGS 4900
4901 GAGAGSGAGAGSGAGSGAGAGSGAGAGYGAGYGAGVGAGYGAGAGYGAGY 4950
4951 GVGAGAGYGAGAGSGAGSGAGAGSGAGAGSGAGAGSGAGAGSGAGAGSGA 5000
5001 GSGAGAGYGAGAGAGYGAGAGAGYGAGAGSGAASGAGAGAGAGSGAGAGS 5050
5051 GAGAGSGAGSGAGAGSGAGAGYGAGAGSGAASGAGAGSGAGAGAGAGAGA 5100
5101 GSGAGAGSGAGAGYGAGAGSGAASGAGAGAGAGTGSSGFGPYVANGGYSR 5150
5151 REGYEYAWSSKSDFETGSGAASGAGAGAGSGAGAGSGAGAGSGAGAGSGA 5200
5201 GAGGSVSYGAGRGYGQGAGSAASSVSSASSRSYDYSRRNVRKNCGIPRRQ 5250
5251 LVVKFRALPCVNC 5263
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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