 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q13029 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MNQNTTEPVAATETLAEVPEHVLRGLPEEVRLFPSAVDKTRIGVWATKPI 50
51 LKGKKFGPFVGDKKKRSQVKNNVYMWEVYYPNLGWMCIDATDPEKGNWLR 100
101 YVNWACSGEEQNLFPLEINRAIYYKTLKPIAPGEELLVWYNGEDNPEIAA 150
151 AIEEERASARSKRSSPKSRKGKKKSQENKNKGNKIQDIQLKTSEPDFTSA 200
201 NMRDSAEGPKEDEEKPSASALEQPATLQEVASQEVPPELATPAPAWEPQP 250
251 EPDERLEAAACEVNDLGEEEEEEEEEDEEEEEDDDDDELEDEGEEEASMP 300
301 NENSVKEPEIRCDEKPEDLLEEPKTTSEETLEDCSEVTPAMQIPRTKEEA 350
351 NGDVFETFMFPCQHCERKFTTKQGLERHMHIHISTVNHAFKCKYCGKAFG 400
401 TQINRRRHERRHEAGLKRKPSQTLQPSEDLADGKASGENVASKDDSSPPS 450
451 LGPDCLIMNSEKASQDTINSSVVEENGEVKELHPCKYCKKVFGTHTNMRR 500
501 HQRRVHERHLIPKGVRRKGGLEEPQPPAEQAQATQNVYVPSTEPEEEGEA 550
551 DDVYIMDISSNISENLNYYIDGKIQTNNNTSNCDVIEMESASADLYGINC 600
601 LLTPVTVEITQNIKTTQVPVTEDLPKEPLGSTNSEAKKRRTASPPALPKI 650
651 KAETDSDPMVPSCSLSLPLSISTTEAVSFHKEKSVYLSSKLKQLLQTQDK 700
701 LTPAGISATEIAKLGPVCVSAPASMLPVTSSRFKRRTSSPPSSPQHSPAL 750
751 RDFGKPSDGKAAWTDAGLTSKKSKLESHSDSPAWSLSGRDERETVSPPCF 800
801 DEYKMSKEWTASSAFSSVCNQQPLDLSSGVKQKAEGTGKTPVQWESVLDL 850
851 SVHKKHCSDSEGKEFKESHSVQPTCSAVKKRKPTTCMLQKVLLNEYNGID 900
901 LPVENPADGTRSPSPCKSLEAQPDPDLGPGSGFPAPTVESTPDVCPSSPA 950
951 LQTPSLSSGQLPPLLIPTDPSSPPPCPPVLTVATPPPPLLPTVPLPAPSS 1000
1001 SASPHPCPSPLSNATAQSPLPILSPTVSPSPSPIPPVEPLMSAASPGPPT 1050
1051 LSSSSSSSSSSSSFSSSSSSSSPSPPPLSAISSVVSSGDNLEASLPMISF 1100
1101 KQEELENEGLKPREEPQSAAEQDVVVQETFNKNFVCNVCESPFLSIKDLT 1150
1151 KHLSIHAEEWPFKCEFCVQLFKDKTDLSEHRFLLHGVGNIFVCSVCKKEF 1200
1201 AFLCNLQQHQRDLHPDKVCTHHEFESGTLRPQNFTDPSKAHVEHMQSLPE 1250
1251 DPLETSKEEEELNDSSEELYTTIKIMASGIKTKDPDVRLGLNQHYPSFKP 1300
1301 PPFQYHHRNPMGIGVTATNFTTHNIPQTFTTAIRCTKCGKGVDNMPELHK 1350
1351 HILACASASDKKRYTPKKNPVPLKQTVQPKNGVVVLDNSGKNAFRRMGQP 1400
1401 KRLNFSVELSKMSSNKLKLNALKKKNQLVQKAILQKNKSAKQKADLKNAC 1450
1451 ESSSHICPYCNREFTYIGSLNKHAAFSCPKKPLSPPKKKVSHSSKKGGHS 1500
1501 SPASSDKNSNSNHRRRTADAEIKMQSMQTPLGKTRARSSGPTQVPLPSSS 1550
1551 FRSKQNVKFAASVKSKKPSSSSLRNSSPIRMAKITHVEGKKPKAVAKNHS 1600
1601 AQLSSKTSRSLHVRVQKSKAVLQSKSTLASKKRTDRFNIKSRERSGGPVT 1650
1651 RSLQLAAAADLSENKREDGSAKQELKDFSYSLRLASRCSPPAAPYITRQY 1700
1701 RKVKAPAAAQFQGPFFKE 1718
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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