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Highlights of Coronavirus Structural Studies

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Reader's Corner Archive

15 Nov

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

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.

6 Oct

Plant receptor-like protein activation by a microbial glycoside hydrolase (Nature)

Plants rely on cell-surface-localized pattern recognition receptors to detect pathogen- or host-derived danger signals and trigger an immune response. Receptor-like proteins (RLPs) with a leucine-rich repeat (LRR) ectodomain constitute a subgroup of pattern recognition receptors and play a critical role in plant immunity. Mechanisms underlying ligand recognition and activation of LRR-RLPs remain elusive. Here we report a crystal structure of the LRR-RLP RXEG1 from Nicotiana benthamiana that recognizes XEG1 xyloglucanase from the pathogen Phytophthora sojae. The structure reveals that specific XEG1 recognition is predominantly mediated by an amino-terminal and a carboxy-terminal loop-out region (RXEG1(ID)) of RXEG1. The two loops bind to the active-site groove of XEG1, inhibiting its enzymatic activity and suppressing Phytophthora infection of N. benthamiana. Binding of XEG1 promotes association of RXEG1(LRR) with the LRR-type co-receptor BAK1 through RXEG1(ID) and the last four conserved LRRs to trigger RXEG1-mediated immune responses. Comparison of the structures of apo-RXEG1(LRR), XEG1–RXEG1(LRR) and XEG1–BAK1–RXEG1(LRR) shows that binding of XEG1 induces conformational changes in the N-terminal region of RXEG1(ID) and enhances structural flexibility of the BAK1-associating regions of RXEG1(LRR). These changes allow fold switching of RXEG1(ID) for recruitment of BAK1(LRR). Our data reveal a conserved mechanism of ligand-induced heterodimerization of an LRR-RLP with BAK1 and suggest a dual function for the LRR-RLP in plant immunity.

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