Research
1. Computational Protein Biophysics
My research in computational protein biophysics aims to elucidate how protein structure and dynamics govern biological function, with a particular emphasis on protein folding, aggregation, and conformational transitions. I have investigated cooperative folding mechanisms, folding transition states, and aggregation-prone structural ensembles using extensive molecular dynamics simulations and free energy–based analyses.
More recently, I have developed a computational framework for predicting structural ensembles of intrinsically disordered proteins (IDPs). This approach enables the characterization of heterogeneous and dynamic conformational populations of IDPs, which are often inaccessible to conventional structure determination methods. By integrating molecular simulations with ensemble-based analysis, my work provides mechanistic insights into how order–disorder transitions and conformational plasticity regulate protein function and interactions.
In parallel, my research addresses protein–protein interactions and signaling dynamics, particularly in G protein-coupled receptors (GPCRs) and their downstream partners. Through combined approaches including molecular dynamics simulations, coevolutionary analysis, and protein design, I have revealed how specific residues, conformational switches, and disordered regions modulate signaling specificity and functional outcomes.
I also perform experimental characterization of protein biophysical properties using techniques such as CD, ITC, DSC, and MST to investigate protein stability, folding behavior, and molecular interactions, thereby complementing and validating computational predictions.
More recently, I have developed a computational framework for predicting structural ensembles of intrinsically disordered proteins (IDPs). This approach enables the characterization of heterogeneous and dynamic conformational populations of IDPs, which are often inaccessible to conventional structure determination methods. By integrating molecular simulations with ensemble-based analysis, my work provides mechanistic insights into how order–disorder transitions and conformational plasticity regulate protein function and interactions.
In parallel, my research addresses protein–protein interactions and signaling dynamics, particularly in G protein-coupled receptors (GPCRs) and their downstream partners. Through combined approaches including molecular dynamics simulations, coevolutionary analysis, and protein design, I have revealed how specific residues, conformational switches, and disordered regions modulate signaling specificity and functional outcomes.
I also perform experimental characterization of protein biophysical properties using techniques such as CD, ITC, DSC, and MST to investigate protein stability, folding behavior, and molecular interactions, thereby complementing and validating computational predictions.
2. Bioinformatics Combined with Machine Learning
I apply machine learning–based bioinformatics approaches to analyze and interpret diverse omics datasets, including transcriptomics and proteomics. My current research focuses on integrating computational methods with data-driven analysis to identify biologically meaningful patterns and regulatory relationships from large-scale omics data.
Using machine learning techniques, I am actively conducting studies that aim to extract key molecular features, gene modules, and functional associations across different biological conditions. These approaches are designed to complement experimental studies and to provide systematic insights into complex biological systems.
Using machine learning techniques, I am actively conducting studies that aim to extract key molecular features, gene modules, and functional associations across different biological conditions. These approaches are designed to complement experimental studies and to provide systematic insights into complex biological systems.
3. Proteomics
I conduct proteomics-based studies to analyze protein-level changes in complex samples. My recent work has applied quantitative proteomics approaches to microplastics-related research, enabling systematic analysis of proteome changes associated with microplastic exposure.