7mcc
From Proteopedia
(Difference between revisions)
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==Crystal structure of an AI-designed TIM-barrel F2C== | ==Crystal structure of an AI-designed TIM-barrel F2C== | ||
| - | <StructureSection load='7mcc' size='340' side='right'caption='[[7mcc]]' scene=''> | + | <StructureSection load='7mcc' size='340' side='right'caption='[[7mcc]], [[Resolution|resolution]] 1.46Å' scene=''> |
== Structural highlights == | == Structural highlights == | ||
| - | <table><tr><td colspan='2'>Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=7MCC OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=7MCC FirstGlance]. <br> | + | <table><tr><td colspan='2'>[[7mcc]] is a 1 chain structure. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=7MCC OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=7MCC FirstGlance]. <br> |
| - | </td></tr><tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[https://proteopedia.org/fgij/fg.htm?mol=7mcc FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=7mcc OCA], [https://pdbe.org/7mcc PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=7mcc RCSB], [https://www.ebi.ac.uk/pdbsum/7mcc PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=7mcc ProSAT]</span></td></tr> | + | </td></tr><tr id='ligand'><td class="sblockLbl"><b>[[Ligand|Ligands:]]</b></td><td class="sblockDat" id="ligandDat"><scene name='pdbligand=SO4:SULFATE+ION'>SO4</scene></td></tr> |
| + | <tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[https://proteopedia.org/fgij/fg.htm?mol=7mcc FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=7mcc OCA], [https://pdbe.org/7mcc PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=7mcc RCSB], [https://www.ebi.ac.uk/pdbsum/7mcc PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=7mcc ProSAT]</span></td></tr> | ||
</table> | </table> | ||
| + | <div style="background-color:#fffaf0;"> | ||
| + | == Publication Abstract from PubMed == | ||
| + | The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design. | ||
| + | |||
| + | Protein sequence design with a learned potential.,Anand N, Eguchi R, Mathews II, Perez CP, Derry A, Altman RB, Huang PS Nat Commun. 2022 Feb 8;13(1):746. doi: 10.1038/s41467-022-28313-9. PMID:35136054<ref>PMID:35136054</ref> | ||
| + | |||
| + | From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.<br> | ||
| + | </div> | ||
| + | <div class="pdbe-citations 7mcc" style="background-color:#fffaf0;"></div> | ||
| + | == References == | ||
| + | <references/> | ||
__TOC__ | __TOC__ | ||
</StructureSection> | </StructureSection> | ||
[[Category: Large Structures]] | [[Category: Large Structures]] | ||
| - | [[Category: Anand-Achim N]] | + | [[Category: Anand-Achim, N]] |
| - | [[Category: Huang | + | [[Category: Huang, P S]] |
| - | [[Category: Mathews | + | [[Category: Mathews, I I]] |
| - | [[Category: Perez | + | [[Category: Perez, C P]] |
| + | [[Category: De novo protein]] | ||
| + | [[Category: Tim-barrel]] | ||
Revision as of 08:40, 23 February 2022
Crystal structure of an AI-designed TIM-barrel F2C
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