>P57105 (145 residues) MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQ EGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPIGHRGEGDPSGIPI FMVLVPVFALTMVAAWAFMRYRQQL |
Sequence |
20 40 60 80 100 120 140 | | | | | | | MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPIGHRGEGDPSGIPIFMVLVPVFALTMVAAWAFMRYRQQL |
Prediction | CCCCCCCCSSSSSSSSSSCCCCCCSSSSCCCCCCCCCCCCCSSSSSSCCCCHHHHHCCCCCCCSSSSSCCSSCCCCCHHHHHHHHHHCCCSSSSSSSSCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC |
Confidence | 9998765338999999968994548997467888877888679999779993686099899939999999778999999999999848996999999657788886667899998888866555887776545232222233359 |
H:Helix; S:Strand; C:Coil | |
Sequence |
20 40 60 80 100 120 140 | | | | | | | MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPIGHRGEGDPSGIPIFMVLVPVFALTMVAAWAFMRYRQQL |
Prediction | 8644564434444030325862100002013445346752100023037613045334044302003014430562316300520472654020102244636754456546553442334343334344333333314425555 |
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 | | | | | | | | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SS Seq | CCCCCCCCSSSSSSSSSSCCCCCCSSSSCCCCCCCCCCCCCSSSSSSCCCCHHHHHCCCCCCCSSSSSCCSSCCCCCHHHHHHHHHHCCCSSSSSSSSCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPIGHRGEGDPSGIPIFMVLVPVFALTMVAAWAFMRYRQQL | |||||||||||||||||||
1 | 3b76B | 0.23 | 0.16 | 4.98 | 1.00 | DEthreader | NL-YF-QSMHEKVVNIQKDGESLGMTVAGGASHR-EWD-LPIYVISVEPGGVISRDGRIKTGDILLNVDGVELTEVSRSEAVALLKRTSSSIVLKALEVKEGSIV---------------------------------------- | |||||||||||||
2 | 2enoA | 0.96 | 0.74 | 20.89 | 1.87 | SPARKS-K | MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPISGPSSG-------------------------------- | |||||||||||||
3 | 2xkxA | 0.32 | 0.31 | 9.34 | 0.74 | MapAlign | ----TEGEMEYEEITLERGNSGLGFSIAGGTDNPHIGDDPSIFITKIIPGGAAAQDGRLRVNDSILFVNEVDVREVTHSAAVEALKEAGSIVRLYVMRRKPPSVREVAEQGKHCILDVSANAVRRLQAAEEQARKAFDRATKL-- | |||||||||||||
4 | 2xkxA | 0.30 | 0.30 | 9.19 | 0.44 | CEthreader | YVNGTEGEMEYEEITLERGNSGLGFSIAGGTDNPHIGDDPSIFITKIIPGGAAAQDGRLRVNDSILFVNEVDVREVTHSAAVEALKEAGSIVRLYVMRRKPPAEKVMEIKLIKGPKGLGFSIAGGVGNQHIPGDNSIYVTKIIEG | |||||||||||||
5 | 2enoA | 0.96 | 0.74 | 20.89 | 1.66 | MUSTER | MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPISGPSSG-------------------------------- | |||||||||||||
6 | 2xkxA | 0.33 | 0.33 | 9.93 | 1.13 | HHsearch | VNGTE-GEMEYEEITLERGNSGLGFSIAGGTDNPHIGDDPSIFITKIIPGGAAAQDGRLRVNDSILFVNEVDVREVTHSAAVEALKEAGSIVRLYVMRRKPPAEKEIKLIKGPKGLGFSIAGGVGDAVAALKNTYVYLKVAKPSN | |||||||||||||
7 | 2enoA | 0.96 | 0.74 | 20.89 | 1.63 | FFAS-3D | MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPISGPSSG-------------------------------- | |||||||||||||
8 | 5v6bA | 0.15 | 0.14 | 4.86 | 0.87 | EigenThreader | EDFIFAHVKQRKEVEVFKSEEALGLTITDN-------GAGYAFIKRIKEGSVIDHIQLISVGDMIEAINGQSLLGCRHYEVARLLKELPRGLKLTEPRKAFGTGRYMGIRDELGKDKRNPDELAEALDERLGEFVFDVWGAIG-D | |||||||||||||
9 | 2enoA | 0.96 | 0.74 | 20.89 | 1.41 | CNFpred | MNGRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQNGPISGPSSG-------------------------------- | |||||||||||||
10 | 2enoA | 0.99 | 0.70 | 19.70 | 1.00 | DEthreader | --GRVDYLVTEEEINLTRGPSGLGFNIVGGTDQQYVSNDSGIYVSRIKENGAAALDGRLQEGDKILSVNGQDLKNLLHQDAVDLFRNAGYAVSLRVQHRLQVQN-G--------------------------------------- | |||||||||||||
|
|
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, 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. |