Crystal structures of FolM alternative dihydrofolate reductase 1 from Brucella suis and Brucella canis

Members of the bacterial genus Brucella cause brucellosis, a zoonotic disease that affects both livestock and wildlife. Brucella are category B infectious agents that can be aerosolized for biological warfare. As part of the structural genomics studies at the Seattle Structural Genomics Center for Infectious Disease (SSGCID), FolM alternative dihydrofolate reductases 1 from Brucella suis and Brucella canis were produced and their structures are reported. The enzymes share ∼95% sequence identity but have less than 33% sequence identity to other homologues with known structure. The structures are prototypical NADPH-dependent short-chain reductases that share their highest tertiary-structural similarity with protozoan pteridine reductases, which are being investigated for rational therapeutic development.

Computational structural genomics unravels common folds and novel families in the secretome of fungal phytopathogen Magnaporthe oryzae

Structural biology has the potential to illuminate the evolution of pathogen effectors and their commonalities that cannot be readily detected at the primary sequence level. Recent breakthroughs in protein structure modeling have demonstrated the feasibility to predict the protein folds without depending on homologous structures. These advances enabled a genome-wide computational structural biology approach to understand proteins based on their predicted folds. In this study, we employed structure prediction methods on the secretome of the destructive fungal pathogen Magnaporthe oryzae. Out of 1854 secreted proteins, we predicted the folds of 1295 (70%) proteins. We showed that template-free modeling by TrRosetta captured 514 folds missed by homology modeling, including many known effectors and virulence factors, and that TrRosetta generally produced higher quality models for secreted proteins. Along with sensitive homology search, we employed structure-based clustering, defining not only homologous groups with divergent members but also sequence-unrelated structural analogous groups. We demonstrate that this approach can reveal potential new members of structurally similar MAX effectors and novel analogous effector families present in M. oryzae and possibly in other phytopathogens. We also investigated the evolution of expanded putative ADP-ribose transferases with predicted structures. We suggest that the loss of catalytic activities of the enzymes might have led them to new evolutionary trajectories to be specialized as protein binders. Collectively, we propose that computational structural genomics approaches can be an integral part of studying effector biology and provide valuable resources that were inaccessible before the advent of machine learning-based structure prediction.

Correction to: Current status and impending progress for cassava structural genomics

A correction to this paper has been published: https://doi.org/10.1007/s11103-021-01139-7

Genomics, Proteomics, and Metabolomics

Genomics could be viewed as the study of the randomness of DNA sequences. It may be possible to predict the structure of a gene product from the nucleotide sequences and thereby predict its function. The terms “structural genomics” and “functional genomics” were coined to denote the assignment of structure and function to a gene product, respectively. Proteomics focuses on the products of gene, which are basically proteins. Proteins are responsible for the development of phenotype, and proteomics is the bridge between genotype and phenotype. The transcribed mRNAs and their abundance are called transcriptome. Proteomics also deals with the interaction between proteins called intractomics. Metabolomics is concerned with identification, abundance, and localization of all the molecules excluding lipids and polysaccharides in the cell. In this chapter, the basic concepts and analysis of the genomic, proteomic, and metabolomics data for their practical utilization are discussed.