>Q16873 (150 residues) MKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYF PLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVA LAALGLLAHFLPAALRAALLGRLRTLLPWA |
Sequence |
20 40 60 80 100 120 140 | | | | | | | MKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPWA |
Prediction | CCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCSHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCC |
Confidence | 996323789999999999999999999999860999998889877768999887655126999999999996137499999967999999999987606955576661589999999999999999999999996156998876507889 |
H:Helix; S:Strand; C:Coil | |
Sequence |
20 40 60 80 100 120 140 | | | | | | | MKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPWA |
Prediction | 657411000001321332233102201410473715314333465142123122111133133333121000333431000002202301331031114336543321220332333133312320033104420344025304521448 |
Values range from 0 (buried residue) to 8 (highly exposed residue) | |
Rank | PDB hit | ID1 | ID2 | Cov | Norm. Zscore | Downloadalignment | 20 40 60 80 100 120 140 | | | | | | | | |||||||||||||
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SS Seq | CCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCSHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCC MKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPWA | |||||||||||||||||||
1 | 2uuiA | 0.97 | 0.95 | 26.54 | 1.33 | DEthreader | HKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPLTT-GPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTL-LP- | |||||||||||||
2 | 2uuiA | 0.99 | 0.99 | 27.63 | 2.32 | SPARKS-K | HKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPW- | |||||||||||||
3 | 2uuiA | 0.95 | 0.87 | 24.50 | 1.08 | MapAlign | ---EVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLP------AALRAALLG--- | |||||||||||||
4 | 2uuiA | 0.99 | 0.99 | 27.63 | 0.92 | CEthreader | HKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPW- | |||||||||||||
5 | 2uuiA | 0.99 | 0.99 | 27.63 | 1.81 | MUSTER | HKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPW- | |||||||||||||
6 | 2uuiA | 0.99 | 0.99 | 27.63 | 4.51 | HHsearch | HKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLLPW- | |||||||||||||
7 | 2q7mC | 0.33 | 0.32 | 9.61 | 2.39 | FFAS-3D | MDQEVVLLAIVTLISVVQNGFFAHKVEHESRTQNGRSFQRTGTLAFERVYTANQNCVDAYPTFLAVLWSAGLLCSQVPAAFAGLMYLFVRQKYFVGYLGERTQSTPGYIFGKRIILFLFLMSVAGIFNYYLIFFFGSDFENYIAT----- | |||||||||||||
8 | 2uuiA | 0.98 | 0.94 | 26.34 | 1.10 | EigenThreader | HKDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGR------LR | |||||||||||||
9 | 2pnoA | 1.00 | 0.97 | 27.25 | 1.22 | CNFpred | -KDEVALLAAVTLLGVLLQAYFSLQVISARRAFRVSPPLTTGPPEFERVYRAQVNCSEYFPLFLATLWVAGIFFHEGAAALCGLVYLFARLRYFQGYARSAQLRLAPLYASARALWLLVALAALGLLAHFLPAALRAALLGRLRTLL--- | |||||||||||||
10 | 6ssrA | 0.46 | 0.40 | 11.67 | 1.33 | DEthreader | -N--SILLAAVSILSACQQSYFALQVGKARLKYKVTAV-T-GSPEFERVFRAQQNCVEFYPIFIITLWMAGWYFNQVFATCLGLVYIYGRHLYFWGYSEAAKKRITGFRLSLGILALLTLLGALGIANSFLDEYLD-------------- | |||||||||||||
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Top 10 structural analogs in PDB (as identified by
TM-align)
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Top 5 enzyme homologs in PDB
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Template proteins with similar binding site:
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References: | |
1. | Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. In preparation, 2020. |