Metformin, an oral antihyperglycemic drug that has been in use for over 60 years, remains a first-line therapy for type 2 diabetes (T2D). Numerous studies have suggested that metformin promotes health benefits beyond T2D management, including weight loss, cancer prevention and treatment, and anti-aging, through several proposed mechanistic targets. Here we discuss the established effects of metformin and the progress made in identifying its direct targets. Additionally, we emphasize the importance of elucidating the structural bases of the drug and its direct targets. Ultimately, this review aims to highlight the current state of knowledge regarding metformin and its related emerging discoveries, while also outlining critical future research directions.

Guanine nucleotides are required for growth and viability of cells due to their structural role in DNA and RNA, and their regulatory roles in translation, signal transduction, and cell division. The natural antibiotic mycophenolic acid (MPA) targets the rate-limiting step in de novo guanine nucleotide biosynthesis executed by inosine-5´-monophosphate dehydrogenase (IMPDH). MPA is used clinically as an immunosuppressant, but whether in vivo inhibition of bacterial IMPDH (GuaB) is a valid antibacterial strategy is controversial. Here, we describe the discovery of extremely potent small molecule GuaB inhibitors (GuaBi) specific to pathogenic bacteria with a low frequency of on-target spontaneous resistance and bactericidal efficacy in vivo against Acinetobacter baumannii mouse models of infection. The spectrum of GuaBi activity includes multidrug-resistant pathogens that are a critical priority of new antibiotic development. Co-crystal structures of A. baumannii, Staphylococcus aureus, and Escherichia coli GuaB proteins bound to inhibitors show comparable binding modes of GuaBi across species and identifies key binding site residues that are predictive of whole-cell activity across both Gram-positive and Gram-negative clades of Bacteria. The clear in vivo efficacy of these small molecule GuaB inhibitors in a model of A. baumannii infection validates GuaB as an essential antibiotic target.
OBJECTIVE: The emergence of multidrug-resistant bacteria worldwide has renewed interest in discovering antibiotics with novel mechanism of action. For the first time ever, we demonstrate that pharmacological inhibition of de novo guanine biosynthesis is bactericidal in a mouse model of Acinetobacter baumannii infection. Structural analyses of novel inhibitors explain differences in biochemical and whole-cell activity across bacterial clades and underscore why this discovery may have broad translational impact on treatment of the most recalcitrant bacterial infections.

The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

Solvatochromic compounds have emerged as valuable environment-sensitive probes for biological research. Here we used thiol-reactive solvatochromic analogs of the green fluorescent protein (GFP) chromophore to track conformational changes in two proteins, recoverin and the A2A adenosine receptor (A2AAR). Two dyes showed Ca2+-induced fluorescence changes when attached to recoverin. Our best-performing dye, DyeC, exhibited agonist-induced changes in both intensity and shape of its fluorescence spectrum when attached to A2AAR; none of these effects were observed with other common environment-sensitive dyes. Molecular dynamics simulations showed that activation of the A2AAR led to a more confined and hydrophilic environment for DyeC. Additionally, an allosteric modulator of A2AAR induced distinct fluorescence changes in the DyeC spectrum, indicating a unique receptor conformation. Our study demonstrated that GFP-inspired dyes are effective for detecting structural changes in G protein-coupled receptors (GPCRs), offering advantages such as intensity-based and ratiometric tracking, redshifted fluorescence spectra, and sensitivity to allosteric modulation.

Identifying and characterizing metal-binding sites (MBS) within macromolecular structures is imperative for elucidating their biological functions. CheckMyMetal (CMM) is a web based tool that facilitates the interactive validation of MBS in structures determined through X-ray crystallography and cryo-electron microscopy (cryo-EM). Recent updates to CMM have significantly enhanced its capability to efficiently handle large datasets generated from cryo-EM structural analyses. In this study, we address various challenges inherent in validating MBS within both X-ray and cryo-EM structures. Specifically, we examine the difficulties associated with accurately identifying metals and modeling their coordination environments by considering the ongoing reproducibility challenges in structural biology and the critical importance of well annotated, high-quality experimental data. CMM employs a sophisticated framework of rules rooted in the valence bond theory for MBS validation. We explore how CMM validation parameters correlate with the resolution of experimentally derived structures of macromolecules and their complexes. Additionally, we showcase the practical utility of CMM by analyzing a representative cryo-EM structure. Through a comprehensive examination of experimental data, we demonstrate the capability of CMM to advance MBS characterization and identify potential instances of metal misassignment.

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind\'s AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

Bombesin receptor subtype-3 (BRS3) is an important orphan G protein-coupled receptor that regulates energy homeostasis and insulin secretion. As a member of the bombesin receptor (BnR) family, the lack of known endogenous ligands and high-resolution structure has hindered the understanding of BRS3 signaling and function. We present two cryogenic electron microscopy (cryo-EM) structures of BRS3 in complex with the heterotrimeric Gq protein in its active states: one bound to the pan-BnR agonist BA1 and the other bound to the synthetic BRS3-specific agonist MK-5046. These structures reveal the architecture of the orthosteric ligand pocket underpinning molecular recognition and provide insights into the structural basis for BRS3\'s selectivity and low affinity for bombesin peptides. Examination of conserved micro-switches suggests a shared activation mechanism among BnRs. Our findings shed light on BRS3\'s ligand selectivity and signaling mechanisms, paving the way for exploring its therapeutic potential for diabetes, obesity, and related metabolic disorders.

Magnesium ions (Mg2+) are crucial in class II terpene cyclases that utilize substrates with diphosphate groups. Interestingly, these enzymes catalyze reactions without cleaving the diphosphate group, instead initiating the reaction through protonation. In our recent research, we discovered a novel class II sesquiterpene cyclase in Streptomyces showdoensis. Notably, we determined its crystal structure and identified Mg2+ within its active site. This finding has shed light on the previously elusive question of Mg2+ binding in class II terpene cyclases. In this chapter, we outline our methods for discovering this novel enzyme, including steps for its purification, crystallization, and kinetic analysis.

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein\'s three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR\'s performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR\'s accuracy. The model\'s predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR\'s predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.