Toward the computational design of protein crystals with improved resolution. Issue 11 (6th November 2019)

• Record Type: Journal Article
• Authors: Jeliazkov, Jeliazko R.; Robinson, Aaron C.; García-Moreno E., Bertrand; Berger, James M.; Gray, Jeffrey J.
• Is Part Of: Acta crystallographica. Volume 75:Issue 11(2019)
• Page Start: 1015
• Page End: 1027
• Publication Date: 2019-11-06
• Subjects: protein crystallography -- protein design -- Rosetta -- staphylococcal nuclease; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S2059798319013226
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : The nature of crystallography of biological macromolecules can be unpredictable, and protein crystals do not consistently produce high‐resolution diffraction data. In the study described here, it was sought to develop a computational design approach that could identify resolution‐enhancing point mutations at crystallographic protein–protein interfaces. Abstract : 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.