6u0x: Difference between revisions
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<StructureSection load='6u0x' size='340' side='right'caption='[[6u0x]], [[Resolution|resolution]] 1.86Å' scene=''> | <StructureSection load='6u0x' size='340' side='right'caption='[[6u0x]], [[Resolution|resolution]] 1.86Å' scene=''> | ||
== Structural highlights == | == Structural highlights == | ||
<table><tr><td colspan='2'>[[6u0x]] is a 1 chain structure. This structure supersedes the now removed PDB entry [http://oca.weizmann.ac.il/oca-bin/send-pdb?obs=1&id=6oka 6oka]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=6U0X OCA]. For a <b>guided tour on the structure components</b> use [ | <table><tr><td colspan='2'>[[6u0x]] is a 1 chain structure with sequence from [https://en.wikipedia.org/wiki/Staphylococcus_aureus Staphylococcus aureus]. This structure supersedes the now removed PDB entry [http://oca.weizmann.ac.il/oca-bin/send-pdb?obs=1&id=6oka 6oka]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=6U0X OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=6U0X FirstGlance]. <br> | ||
</td></tr><tr id=' | </td></tr><tr id='method'><td class="sblockLbl"><b>[[Empirical_models|Method:]]</b></td><td class="sblockDat" id="methodDat">X-ray diffraction, [[Resolution|Resolution]] 1.86Å</td></tr> | ||
<tr id=' | <tr id='ligand'><td class="sblockLbl"><b>[[Ligand|Ligands:]]</b></td><td class="sblockDat" id="ligandDat"><scene name='pdbligand=CA:CALCIUM+ION'>CA</scene>, <scene name='pdbligand=THP:THYMIDINE-3,5-DIPHOSPHATE'>THP</scene></td></tr> | ||
<tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[ | <tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[https://proteopedia.org/fgij/fg.htm?mol=6u0x FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=6u0x OCA], [https://pdbe.org/6u0x PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=6u0x RCSB], [https://www.ebi.ac.uk/pdbsum/6u0x PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=6u0x ProSAT]</span></td></tr> | ||
</table> | </table> | ||
== Function == | == Function == | ||
[ | [https://www.uniprot.org/uniprot/NUC_STAAU NUC_STAAU] Enzyme that catalyzes the hydrolysis of both DNA and RNA at the 5' position of the phosphodiester bond. | ||
<div style="background-color:#fffaf0;"> | |||
== Publication Abstract from PubMed == | |||
Substantial advances have been made in the computational design of protein interfaces over the last 20 years. However, the interfaces targeted by design have typically been stable and high-affinity. Here, we report the development of a generic computational design method to stabilize the weak interactions at crystallographic interfaces. Initially, we analyzed structures reported in the Protein Data Bank to determine whether crystals with more stable interfaces result in higher resolution structures. We found that for 22 variants of a single protein crystallized by a single individual, the Rosetta-calculated `crystal score' correlates with the reported diffraction resolution. We next developed and tested a computational design protocol, seeking to identify point mutations that would improve resolution in a highly stable variant of staphylococcal nuclease (SNase). Using a protocol based on fixed protein backbones, only one of the 11 initial designs crystallized, indicating modeling inaccuracies and forcing us to re-evaluate our strategy. To compensate for slight changes in the local backbone and side-chain environment, we subsequently designed on an ensemble of minimally perturbed protein backbones. Using this strategy, four of the seven designed proteins crystallized. By collecting diffraction data from multiple crystals per design and solving crystal structures, we found that the designed crystals improved the resolution modestly and in unpredictable ways, including altering the crystal space group. Post hoc, in silico analysis of the three observed space groups for SNase showed that the native space group was the lowest scoring for four of six variants (including the wild type), but that resolution did not correlate with crystal score, as it did in the preliminary results. Collectively, our results show that calculated crystal scores can correlate with reported resolution, but that the correlation is absent when the problem is inverted. This outcome suggests that more comprehensive modeling of the crystallographic state is necessary to design high-resolution protein crystals from poorly diffracting crystals. | |||
Toward the computational design of protein crystals with improved resolution.,Jeliazkov JR, Robinson AC, Garcia-Moreno E B, Berger JM, Gray JJ Acta Crystallogr D Struct Biol. 2019 Nov 1;75(Pt 11):1015-1027. doi:, 10.1107/S2059798319013226. Epub 2019 Nov 1. PMID:31692475<ref>PMID:31692475</ref> | |||
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.<br> | |||
</div> | |||
<div class="pdbe-citations 6u0x" style="background-color:#fffaf0;"></div> | |||
==See Also== | |||
*[[Staphylococcal nuclease 3D structures|Staphylococcal nuclease 3D structures]] | |||
== References == | |||
<references/> | |||
__TOC__ | __TOC__ | ||
</StructureSection> | </StructureSection> | ||
[[Category: Large Structures]] | [[Category: Large Structures]] | ||
[[Category: | [[Category: Staphylococcus aureus]] | ||
[[Category: Berger | [[Category: Berger JM]] | ||
[[Category: | [[Category: Garcia-Moreno E B]] | ||
[[Category: Gray | [[Category: Gray JG]] | ||
[[Category: Jeliazkov | [[Category: Jeliazkov JR]] | ||
[[Category: Robinson | [[Category: Robinson AC]] | ||
Latest revision as of 10:41, 11 October 2023
Crystal structure of Staphylococcal nuclease variant Delta+PHS Q123D at cryogenic temperatureCrystal structure of Staphylococcal nuclease variant Delta+PHS Q123D at cryogenic temperature
Structural highlights
FunctionNUC_STAAU Enzyme that catalyzes the hydrolysis of both DNA and RNA at the 5' position of the phosphodiester bond. Publication Abstract from PubMedSubstantial advances have been made in the computational design of protein interfaces over the last 20 years. However, the interfaces targeted by design have typically been stable and high-affinity. Here, we report the development of a generic computational design method to stabilize the weak interactions at crystallographic interfaces. Initially, we analyzed structures reported in the Protein Data Bank to determine whether crystals with more stable interfaces result in higher resolution structures. We found that for 22 variants of a single protein crystallized by a single individual, the Rosetta-calculated `crystal score' correlates with the reported diffraction resolution. We next developed and tested a computational design protocol, seeking to identify point mutations that would improve resolution in a highly stable variant of staphylococcal nuclease (SNase). Using a protocol based on fixed protein backbones, only one of the 11 initial designs crystallized, indicating modeling inaccuracies and forcing us to re-evaluate our strategy. To compensate for slight changes in the local backbone and side-chain environment, we subsequently designed on an ensemble of minimally perturbed protein backbones. Using this strategy, four of the seven designed proteins crystallized. By collecting diffraction data from multiple crystals per design and solving crystal structures, we found that the designed crystals improved the resolution modestly and in unpredictable ways, including altering the crystal space group. Post hoc, in silico analysis of the three observed space groups for SNase showed that the native space group was the lowest scoring for four of six variants (including the wild type), but that resolution did not correlate with crystal score, as it did in the preliminary results. Collectively, our results show that calculated crystal scores can correlate with reported resolution, but that the correlation is absent when the problem is inverted. This outcome suggests that more comprehensive modeling of the crystallographic state is necessary to design high-resolution protein crystals from poorly diffracting crystals. Toward the computational design of protein crystals with improved resolution.,Jeliazkov JR, Robinson AC, Garcia-Moreno E B, Berger JM, Gray JJ Acta Crystallogr D Struct Biol. 2019 Nov 1;75(Pt 11):1015-1027. doi:, 10.1107/S2059798319013226. Epub 2019 Nov 1. PMID:31692475[1] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. See AlsoReferences
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