| >A9QM74 (139 residues) SNKRIGQVVNTGVLPRLVVLMTSSELNVLTPSLRTVGNIVTGTDEQTQMAIDAGMLNVLP QLLQHNKPSIQKEAAWALSNVAAGPCHHIQQLLAYDVLPPLVALLKNGEFKVQKEAVWMV ANFATGATMDQLIQLVHSG |
| Sequence |
20 40 60 80 100 120 | | | | | | SNKRIGQVVNTGVLPRLVVLMTSSELNVLTPSLRTVGNIVTGTDEQTQMAIDAGMLNVLPQLLQHNKPSIQKEAAWALSNVAAGPCHHIQQLLAYDVLPPLVALLKNGEFKVQKEAVWMVANFATGATMDQLIQLVHSG |
| Prediction | CCHHHHHHHHCCCHHHHHHHHCCCCHHHHHHHHHHHHHHHCCCHHHHHHHHHCCHHHHHHHHHCCCCHHHHHHHHHHHHHHHCCCHHHHHHHHHCCCHHHHHHHHHCCCHHHHHHHHHHHHHHHCCCCHHHHHHHHHCC |
| Confidence | 9389999999097799999975999999999999999997198699999999181899999974999999999999999996799999999999186999999995599999999999999997199999999999856 |
| H:Helix; S:Strand; C:Coil | |
| Sequence |
20 40 60 80 100 120 | | | | | | SNKRIGQVVNTGVLPRLVVLMTSSELNVLTPSLRTVGNIVTGTDEQTQMAIDAGMLNVLPQLLQHNKPSIQKEAAWALSNVAAGPCHHIQQLLAYDVLPPLVALLKNGEFKVQKEAVWMVANFATGATMDQLIQLVHSG |
| Prediction | 8673141016250033005035373450121002000100223661131025240041014005374540122001000100233761142026460033004205543250121001000100332346104302738 |
| 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 | | | | | | | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SS Seq | CCHHHHHHHHCCCHHHHHHHHCCCCHHHHHHHHHHHHHHHCCCHHHHHHHHHCCHHHHHHHHHCCCCHHHHHHHHHHHHHHHCCCHHHHHHHHHCCCHHHHHHHHHCCCHHHHHHHHHHHHHHHCCCCHHHHHHHHHCC SNKRIGQVVNTGVLPRLVVLMTSSELNVLTPSLRTVGNIVTGTDEQTQMAIDAGMLNVLPQLLQHNKPSIQKEAAWALSNVAAGPCHHIQQLLAYDVLPPLVALLKNGEFKVQKEAVWMVANFATGATMDQLIQLVHSG | |||||||||||||||||||
| 1 | 4rv1A | 0.36 | 0.35 | 10.51 | 1.50 | DEthreader | GASAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPD-EAIKAIVDGG | |||||||||||||
| 2 | 4hxtA2 | 0.34 | 0.32 | 9.49 | 1.37 | SPARKS-K | PDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPTSAIKAIVDAGGVEVLQKLLTSTDSEVQKEAQRALENIKSGGWLEH-------- | |||||||||||||
| 3 | 4rv1A2 | 0.36 | 0.35 | 10.50 | 0.66 | MapAlign | -DEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGP-TSAIKAIVDAG | |||||||||||||
| 4 | 4hxtA | 0.36 | 0.35 | 10.51 | 0.44 | CEthreader | PASAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASG-PDEAIKAIVDAG | |||||||||||||
| 5 | 1ialA | 0.51 | 0.51 | 14.79 | 1.19 | MUSTER | PNERIEMVVKKGVVPQLVKLLGATELPIVTPALRAIGNIVTGTDEQTQKVIDAGALAVFPSLLTNPKTNIQKEATWTMSNITAGRQDQIQQVVNHGLVPFLVGVLSKADFKTQKEAAWAITNYTSGGTVEQIVYLVHCG | |||||||||||||
| 6 | 1wa5B2 | 0.42 | 0.42 | 12.46 | 0.82 | HHsearch | PQEAIQAVIDVRIPKRLVELLSHESTLVQTPALRAVGNIVTGNDLQTQVVINAGVLPALRLLLSSPKENIKKEACWTISNITAGNTEQIQAVIDANLIPPLVKLLEVAEYKTKKEACWAISNASSGGLPDIIRYLVSQG | |||||||||||||
| 7 | 4hxtA2 | 0.34 | 0.32 | 9.49 | 1.92 | FFAS-3D | PDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPDEAIKAIVDAGGVEVLVKLLTSTDSEVQKEAARALANIASGPTSAIKAIVDAGGVEVLQKLLTSTDSEVQKEAQRALENIKSGGWLEH-------- | |||||||||||||
| 8 | 4uaeA | 0.41 | 0.41 | 12.07 | 0.68 | EigenThreader | GNEQIQMVIDSGIVPHLVPLLSHQEVKVQTAALRAVGNIVTGTDEQTQVVLNCDALSHFPALLTHPKEKINKEAVWFLSNITAGNQQQVQAVIDANLVPMIIHLLDKGDFGTQKEAAWAISNLTISGRKDQVAYLIQQN | |||||||||||||
| 9 | 4e4vA | 0.55 | 0.55 | 15.76 | 1.18 | CNFpred | PNERIGMVVKTGVVPQLVKLLGASELPIVTPALRAIGNIVTGTDEQTQVVIDAGALAVFPSLLTNPKTNIQKEATWTMSNITAGRQDQIQQVVNHGLVPFLVSVLSKADFKTQKEAVWAVTNYTSGGTVEQIVYLVHCG | |||||||||||||
| 10 | 1wa5B2 | 0.42 | 0.42 | 12.27 | 1.50 | DEthreader | GPEAIQAVIDVRIPKRLVELLSHESTLVQTPALRAVGNIVTGNDLQTQVVINAGVLPALRLLLSSPKENIKKEACWTISNITAGNTEQIQAVIDANLIPPLVKLLEVAEYKTKKEACWAISNASSGGRPDIIRYLVSGC | |||||||||||||
| ||||||||||||||||||||
|
Top 10 structural analogs in PDB (as identified by
TM-align)
|
|
Top 5 enzyme homologs in PDB
|
Template proteins with similar binding site:
|
| References: | |
| 1. | Wei Zheng, Qiqige Wuyun, Yang Li, Quancheng Liu, Xiaogen Zhou, Yiheng Zhu, P. Lydia Freddolino, Yang Zhang. Integrating deep learning potentials with I-TASSER for single- and multi-domain protein structure prediction. Submitted. 2023. |