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Highlights of Coronavirus Structural Studies

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Reader's Corner Archive

6 Dec 2022

Recognition of cyclic dinucleotides https://www.nature.com/articles/s41586-022-05452-zand folates by human SLC19A1 (Nature)

Cyclic dinucleotides (CDNs) are ubiquitous signalling molecules in all domains of life. Mammalian cells produce one CDN, 2′3′-cGAMP, through cyclic GMP–AMP synthase after detecting cytosolic DNA signals. 2′3′-cGAMP, as well as bacterial and synthetic CDN analogues, can act as second messengers to activate stimulator of interferon genes (STING) and elicit broad downstream responses. Extracellular CDNs must traverse the cell membrane to activate STING, a process that is dependent on the solute carrier SLC19A. Moreover, SLC19A1 represents the major transporter for folate nutrients and antifolate therapeutics, thereby placing SLC19A1 as a key factor in multiple physiological and pathological processes. How SLC19A1 recognizes and transports CDNs, folate and antifolate is unclear. Here we report cryo-electron microscopy structures of human SLC19A1 (hSLC19A1) in a substrate-free state and in complexes with multiple CDNs from different sources, a predominant natural folate and a new-generation antifolate drug. The structural and mutagenesis results demonstrate that hSLC19A1 uses unique yet divergent mechanisms to recognize CDN- and folate-type substrates. Two CDN molecules bind within the hSLC19A1 cavity as a compact dual-molecule unit, whereas folate and antifolate bind as a monomer and occupy a distinct pocket of the cavity. Moreover, the structures enable accurate mapping and potential mechanistic interpretation of hSLC19A1 with loss-of-activity and disease-related mutations. Our research provides a framework for understanding the mechanism of SLC19-family transporters and is a foundation for the development of potential therapeutics.

15 Nov 2022

Single-cell genomic variation induced by mutational processes in cancer (Nature)

How cell-to-cell copy number alterations that underpin genomic instability in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer, remains understudied. Here, by applying scaled single-cell whole-genome sequencing to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct ‘foreground’ mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.

15 Nov 2022

NMR-guided directed evolution (Nature)

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzym. Machine learning methods have recently showed promise in predicting productive mutations, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

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