Biology subjects

Minh, D.

Publications and source records attributed to Minh, D..

3 recordsLinked to original sources

bioRxiv2025

Enzyme kinetics model for the coronavirus main protease including dimerization and ligand binding

The coronavirus main protease (MPro) plays a pivotal role in viral replication and is the target of several antivirals against SARS-CoV-2. In some species, CRCs of MPro enzymatic activity can exhibit biphasic behavior in which low ligand concentrations activate the enzyme whereas higher ones inhibit it. While this behavior has been attributed to ligand-induced dimerization, quantitative enzyme kinetics models have not been fit to it. Here, we develop a kinetic model integrating dimerization and ligand binding. We perform a Bayesian regression to globally fit the model to multiple types of biochemical and biophysical data. The reversible covalent inhibitor GC376 strongly induces dimerization and binds to the dimer with no cooperativity. In contrast, the fluorescent peptide substrate has a minor effect on dimerization but binds to the dimer with positive cooperativity. The biphasic concentration response curve occurs because compared to substrate, the inhibitor accelerates turnover in the opposite catalytic site.

biochemistry↗

bioRxiv2024

Targeting Unique Ligand Binding Domain Structural Features Downregulates DKK1 in Y537S ESR1 Mutant Breast Cancer Cells

Resistance to endocrine therapies remains a major clinical hurdle in breast cancer. Mutations to estrogen receptor alpha (ER) arise after continued therapeutic pressure. Next generation selective estrogen receptor modulators and degraders/downregulators (SERMs and SERDs) show clinical efficacy, but responses are often non-durable. A tyrosine to serine point mutation at position 537 in the ER ligand binding domain (LBD) is among the most common and most pathogenic alteration in this setting. It enables endocrine therapy resistance by superceding intrinsic structural-energetic gatekeepers of ER hormone-dependence, it enhances metastatic burden by enabling neomorphic ER-dependent transcriptional programs, and it resists SERM and SERD inhibiton by reducing their binding affinities and abilities to antagonize transcriptional coregulator binding. However, a subset of SERMs and SERDs can achieve efficacy by adopting poses that force the mutation to engage in a new interaction that favors the therapeutic receptor antagonist conformation. We previously described a chemically unconventional SERM, T6I-29, that demonstrates significant anti-proliferative activities in Y537S ER breast cancer cells. Here, we use a comprehensive suite of structural-biochemical, in vitro, and in vivo approaches to better T6I-29s activities in breast cancer cells harboring Y537S ER. RNA sequencing in cells treated with T6I-29 reveals a neomorphic downregulation of DKK1, a secreted glycoprotein known to play oncogenic roles in other cancers. Importantly, we find that DKK1 is significantly enriched in ER+ breast cancer plasma compared to healthy controls. This study shows how new SERMs and SERDs can identify new therapeutic pathways in endocrine-resistant ER+ breast cancers.

cancer biology↗

bioRxiv2024

Intracellular pocket conformations determine signaling through the μ opioid receptor

It has been challenging to determine how a ligand that binds to a receptor activates downstream signaling pathways and to predict the strength of signaling. The challenge is compounded by functional selectivity, in which a single ligand binding to a single receptor can activate multiple signaling pathways at different levels. Spectroscopic studies show that in the largest class of cell surface receptors, 7 transmembrane receptors (7TMRs), activation is associated with ligand-induced shifts in the equilibria of intracellular pocket conformations in the absence of transducer proteins. We hypothesized that signaling through the opioid receptor, a prototypical 7TMR, is linearly proportional to the equilibrium probability of observing intracellular pocket conformations in the receptor-ligand complex. Here we show that a machine learning model based on this hypothesis accurately calculates the efficacy of both G protein and {beta}-arrestin-2 signaling. Structural features that the model associates with activation are intracellular pocket expansion, toggle switch rotation, and sodium binding pocket collapse. Distinct pathways are activated by different arrangements of the ligand and sodium binding pockets and the intracellular pocket. While recent work has categorized ligands as active or inactive (or partially active) based on binding affinities to two conformations, our approach accurately computes signaling efficacy along multiple pathways.

biophysics↗