In this paper we describe the engineering and X-ray crystal structure of Thermal Green Protein (TGP), an extremely stable, highly soluble, non-aggregating green fluorescent protein. TGP is a soluble variant of the fluorescent protein eCGP123, which despite being highly stable, has proven to be aggregation-prone. The X-ray crystal structure of eCGP123, also determined within the context of this paper, was used to carry out rational surface engineering to improve its solubility, leading to TGP. The approach involved simultaneously eliminating crystal lattice contacts while increasing the overall negative charge of the protein. Despite intentional disruption of lattice contacts and introduction of high entropy glutamate side chains, TGP crystallized readily in a number of different conditions and the X-ray crystal structure of TGP was determined to 1.9 Å resolution. The structural reasons for the enhanced stability of TGP and eCGP123 are discussed. We demonstrate the utility of using TGP as a fusion partner in various assays and significantly, in amyloid assays in which the standard fluorescent protein, EGFP, is undesirable because of aberrant oligomerization.

Potassium ion channels utilize a highly selective filter to rapidly transport K+ ions across cellular membranes. This selectivity filter is composed of four binding sites which display almost equal electron density in crystal structures with high potassium ion concentrations. This electron density can be interpreted to reflect a superposition of alternating potassium ion and water occupied states or as adjacent potassium ions. Here, we use single wavelength anomalous dispersion (SAD) X-ray diffraction data collected near the potassium absorption edge to show experimentally that all ion binding sites within the selectivity filter are fully occupied by K+ ions. These data support the hypothesis that potassium ion transport occurs by direct Coulomb knock-on, and provide an example of solving the phase problem by K-SAD.

It is demonstrated that using three-dimensional profile fitting of Bragg peaks increases the accuracy and resolution of neutron crystallographic data collected from proteins and reveals new features in nuclear density maps calculated from these data.

The monobactam antibiotic aztreonam is used to treat cystic fibrosis patients with chronic pulmonary infections colonized by Pseudomonas aeruginosa strains expressing CTX-M extended-spectrum β-lactamases. The protonation states of active-site residues that are responsible for hydrolysis have been determined previously for the apo form of a CTX-M β-lactamase but not for a monobactam acyl-enzyme intermediate. Here we used neutron and high-resolution X-ray crystallography to probe the mechanism by which CTX-M extended-spectrum β-lactamases hydrolyze monobactam antibiotics. In these first reported structures of a class A β-lactamase in an acyl-enzyme complex with aztreonam, we directly observed most of the hydrogen atoms (as deuterium) within the active site. Although Lys 234 is fully protonated in the acyl intermediate, we found that Lys 73 is neutral. These findings are consistent with Lys 73 being able to serve as a general base during the acylation part of the catalytic mechanism, as previously proposed.

The emergence and dissemination of bacterial resistance to β-lactam antibiotics via β-lactamase enzymes is a serious problem in clinical settings, often leaving few treatment options for infections resulting from multidrug-resistant superbugs. Understanding the catalytic mechanism of βlactamases is important for developing strategies to overcome resistance. Binding of a substrate in the active site of an enzyme can alter the conformations and pK a s of catalytic residues, thereby contributing to enzyme catalysis. Here we report X-ray and neutron crystal structures of the class A Toho-1 β-lactamase in the apo form and an X-ray structure of a Michaelis-like complex with the cephalosporin antibiotic cefotaxime in the active site. Comparison of these structures reveals that substrate binding induces a series of changes. The side chains of conserved residues important in catalysis, Lys73 and Tyr105, and the main chain of Ser130 alter their conformations, with Nζ of Lys73 moving closer to the position of the conserved catalytic nucleophile Ser70. This movement of Lys73 closer to Ser70 is consistent with proton transfer between the two residues prior to acylation. In combination with the tightly bound catalytic water molecule located between Glu166 and the position of Ser70, the enzyme is primed for catalysis when Ser70 is activated for nucleophilic attack of the β-lactam ring. Quantum mechanical/molecular mechanical (QM/MM) free energy simulations of models of the wild-type enzyme show that proton transfer from the Nζ of Lys73 to the Oε2 atom of Glu166 is more thermodynamically favorable than when it is absent. Taken together, our findings indicate that substrate binding enhances the favorability of the initial proton transfer steps that precede the formation of the acyl-enzyme intermediate.

Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data. research papers J. Appl. Cryst. (2019). 52, 854-863 Brendan Sullivan et al. BraggNet: integrating Bragg peaks using neural networks 855

Lentiviral vectors (LV) have emerged as a robust technology for therapeutic gene delivery into human cells as advanced medicinal products. As these products are increasingly commercialized, there are concomitant demands for their characterization to ensure safety, efficacy and consistency. Standards are essential for accurately measuring parameters for such product characterization. A critical parameter is the vector copy number (VCN) which measures the genetic dose of a transgene present in gene-modified cells. Here we describe a set of clonal Jurkat cell lines with defined copy numbers of a reference lentiviral vector integrated into their genomes. Genomic DNA was characterized for copy number, genomic integrity and integration coordinates and showed uniform performance across independent quantitative PCR assays. Stability studies during continuous long-term culture demonstrated sustained renewability of the reference standard source material. DNA from the Jurkat VCN standards would be useful for control of quantitative PCR assays for VCN determination in LV gene-modified cellular products and clinical samples.

The application of the cryogenic data-collection environments used in protein X-ray crystallography to neutron protein crystallography is discussed.