 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O97676 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MDEPPFTEAALEQALAEPCELDAALLTDIEDMLQLINNQDSDFPGLFDAP 50
51 YAGVAGGTDPTSPDASSPGSPTPPPSTMSSPLEGFLGGARTPPPPPVSPT 100
101 QPAPTPLKMYPSVPAFSPGPGIKEEPVPLTILQPPTPQPLSGALLPQSLP 150
151 ALAPPQLSPAPVLGYPSPPGSFSSATPPGSTSQTLPGLPLASLPGVLPVS 200
201 VHTQVQSAAPQQLLTATATPVVSPGTTTVTSQIQQVPVLLQPHFIKADSL 250
251 LLTTMKTDMGTPVKAAGIGSLAPGTAVQAAPLQTLVSGGTILATVPLVVD 300
301 TDKLPINRLAAGGKALSSGQSRGEKRTAHNAIEKRYRSSINDKIIELKDL 350
351 VVGTEAKLNKSAVLRKAIDYIRFLQQSNQKLKQENLSLRTAAHKSKSLKD 400
401 LVSCSSGGRTDVPMEGVKPEVVDTLSPPPSDAGSPSQSSPLSLGSRGSSS 450
451 GGSGSDSEPDSPVFEDSQMKPEQLPAPHGRGMLDRSRLALCVLVFLCLSC 500
501 NPLASLMGSWALPGPSDATSAYHGPWRSVLGAEGRDGPGWVLWLLPPLVW 550
551 LTNGLLVLLFLALLFVYGEPVTRPHSDPAVRFWRHRKQADLDLARGDFAQ 600
601 AAQQLWLALRALGRPLPTSHLDLACSLLWNLIRHLLQRLWVGRWLAGRAG 650
651 GLRRDSALEADTRTSACDAALVYHKLHQLHTMGKYPGGHLDAANLALSAL 700
701 NLAECAGDALSVAVLAEVYVAAALRVKTSLPRALHFLTRFFLSSARQACL 750
751 AQSGSVPVAMQWLCHPVGHRFFVDGDWAVCGAPRDSLYSVAGNPVDPLAQ 800
801 VTQLFREHLLEQALHCVAQPSPGPGSADGDREFSEALGFLQLLNSCCDTA 850
851 GAPACSFSIASSTAATAGTDPVAKWWASLTAVVTHWLGRDEEAAERLYPL 900
901 VEHLPRALQESERPLPRAALHSFKAARALLGRGKAESGSASLAMCEKASG 950
951 YLQDSLAATPAGSSIDKAMQLLLCDLLLVARTSLWQRQKPPPPSQASQGS 1000
1001 SSGAQASALELRGFQRDLSGLRRLAQSFRPAMRRVFLHEATARLMAGASP 1050
1051 ARTHQLLDRSLRRRAGPCGRGGAAAAAAAELEPRPTRREQAEALLLASCY 1100
1101 LPPGFLSAPGQRVGMLAEAARTLEKLGDRRLLHDCQQMLMRLGGGTTVTS 1150
1151 S 1151
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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