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| | ==De novo designed Rossmann fold protein ROS2_835== | | ==De novo designed Rossmann fold protein ROS2_835== |
| - | <StructureSection load='6vga' size='340' side='right'caption='[[6vga]], [[NMR_Ensembles_of_Models | 20 NMR models]]' scene=''> | + | <StructureSection load='6vga' size='340' side='right'caption='[[6vga]]' scene=''> |
| | == Structural highlights == | | == Structural highlights == |
| - | <table><tr><td colspan='2'>[[6vga]] is a 1 chain structure with sequence from [http://en.wikipedia.org/wiki/Synthetic_construct_sequences Synthetic construct sequences]. Full experimental information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=6VGA OCA]. For a <b>guided tour on the structure components</b> use [http://proteopedia.org/fgij/fg.htm?mol=6VGA FirstGlance]. <br> | + | <table><tr><td colspan='2'>[[6vga]] is a 1 chain structure with sequence from [https://en.wikipedia.org/wiki/Synthetic_construct Synthetic construct]. Full experimental information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=6VGA OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=6VGA FirstGlance]. <br> |
| - | </td></tr><tr id='related'><td class="sblockLbl"><b>[[Related_structure|Related:]]</b></td><td class="sblockDat">[[6vg7|6vg7]]</td></tr> | + | </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=6vga FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=6vga OCA], [https://pdbe.org/6vga PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=6vga RCSB], [https://www.ebi.ac.uk/pdbsum/6vga PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=6vga ProSAT]</span></td></tr> |
| - | <tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[http://proteopedia.org/fgij/fg.htm?mol=6vga FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=6vga OCA], [http://pdbe.org/6vga PDBe], [http://www.rcsb.org/pdb/explore.do?structureId=6vga RCSB], [http://www.ebi.ac.uk/pdbsum/6vga PDBsum], [http://prosat.h-its.org/prosat/prosatexe?pdbcode=6vga ProSAT]</span></td></tr> | + | |
| | </table> | | </table> |
| | <div style="background-color:#fffaf0;"> | | <div style="background-color:#fffaf0;"> |
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| | </StructureSection> | | </StructureSection> |
| | [[Category: Large Structures]] | | [[Category: Large Structures]] |
| - | [[Category: Synthetic construct sequences]] | + | [[Category: Synthetic construct]] |
| - | [[Category: Kelly, M]] | + | [[Category: Kelly M]] |
| - | [[Category: Kortemme, T]] | + | [[Category: Kortemme T]] |
| - | [[Category: Pan, X]] | + | [[Category: Pan X]] |
| - | [[Category: Zhang, Y]] | + | [[Category: Zhang Y]] |
| - | [[Category: De novo protein]]
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| - | [[Category: Rossmann fold]]
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| Structural highlights
Publication Abstract from PubMed
Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature's ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.
Expanding the space of protein geometries by computational design of de novo fold families.,Pan X, Thompson MC, Zhang Y, Liu L, Fraser JS, Kelly MJS, Kortemme T Science. 2020 Aug 28;369(6507):1132-1136. doi: 10.1126/science.abc0881. PMID:32855341[1]
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
References
- ↑ Pan X, Thompson MC, Zhang Y, Liu L, Fraser JS, Kelly MJS, Kortemme T. Expanding the space of protein geometries by computational design of de novo fold families. Science. 2020 Aug 28;369(6507):1132-1136. doi: 10.1126/science.abc0881. PMID:32855341 doi:http://dx.doi.org/10.1126/science.abc0881
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