Research Interests

Hypoxia-inducible factor (HIF) is a protein that helps cells respond to changes in oxygen levels. It is made up of two parts, HIF-α and HIF-β, and its stability is controlled by enzymes called prolyl hydroxylases (PHDs or HIFP4H). Under normal oxygen conditions, PHDs promote the breakdown of HIF, keeping its activity low. However, when PHDs are inhibited, HIF becomes stable and active, which can help treat conditions like ischemic diseases and oxidative stress-related disorders. Despite the potential, many drugs targeting PHDs have shown side effects, such as liver issues and high blood pressure. To address these challenges, our research focuses on exploring the chemical space of natural products as potential PHD inhibitors. We aim to identify new small-molecule drugs that selectively target PHD isoforms and design their biosynthetic pathways in collaboration with experimental research groups to develop safer and more effective therapies.

Retinol-binding proteins (RBPs) play a critical role in the uptake, transport, and metabolism of dietary retinoids (vitamin A and its derivatives). These proteins are implicated in various diseases and metabolic syndromes, including obesity, cardiovascular diseases, and macular degeneration. Interestingly, recent findings revealed that RBPs also interact with lipids, suggesting their broader role in lipid metabolism and signaling. Despite this, the molecular mechanisms underlying the distinct binding of RBPs to retinoids and non-retinoids remain largely unknown. To address this gap, our research aims to unravel these molecular mechanisms. Building on our findings, we also seek to discover a novel class of RBP antagonists to selectively modulate their activity in disease states.

Exploring biomolecular dynamics often involves overcoming challenges posed by energy landscapes with multiple metastable states separated by large energy barriers. These barriers slow down molecular dynamics (MD) simulations and can lead to significant statistical errors when measuring important properties. To address these challenges, enhanced sampling techniques that use collective variables (CVs) have become standard practice. However, identifying good CVs for complex biomolecular systems is not trivial when relying solely on physical intuition. To simplify this process, Oh et al. developed a new method called gradient descent-based multiclass harmonic linear discriminant analysis (GD-HLDA). This approach uses harmonic mean approaches for both within-class and between-class variances and relies on gradient descent on the Stiefel manifold to automatically identify the orthonormal basis vectors for the linear transformation. Our research aims to establish a solid theoretical foundation for GD-HLDA, optimize the method further to enhance its accuracy and efficiency, and validate its broad applicability and high interpretability across a wide range of chemical and biological systems.