With the time as an Associate Professor at National Chung Cheng University and prior academic roles, I specialize in computational biophysics, molecular dynamics simulation, and protein structure prediction. My work leverages physicochemical principles to explore molecular-level biological processes and their implications for neurodegenerative diseases and protein-DNA interactions.
At CCU, I focus on using advanced computational techniques to analyze protein-protein and protein-DNA interactions, combining atomistic and coarse-grained molecular simulations. My aim is to contribute to solving critical biochemical challenges by elucidating the structural and dynamic relationships in functional biomolecular systems.
Research in MYTLab is primarily at the interface of physics, chemistry, and biology. We employ physical and chemical theories to understand biological processes from a molecular perspective. Our research focuses on computational studies on protein-protein/protein-nucleic acids interactions using multi-scale molecular dynamics (MD) simulations (e.g., all-atom and coarse-graining approach). We aim to understand how biological specificity influences a cell’s function at the molecular level. Find out how you can contribute.
Protein Aggregation & Amyloid Dynamics
– Developed a mechanistic framework integrating coarse-grained molecular simulations and stochastic modeling to dissect amyloid aggregation. Identified how monomer diffusion (Aβ42), peptide morphology (proline-substituted oligomers), and surface-guided nucleation collectively regulate the kinetics of fibril growth. Advanced theoretical tools by introducing the method of second stochasticization to model aggregation as ensembles of reactive trajectories. (Protein Sci. 2025; JPCB 2021/2023; Front Mol. Biosci. 2021)
– Integrated nucleation theory with coarse-grained MD to explain amyloid formation and energy landscapes of Aβ. (PNAS 2016, JACS 2017; ∼270 citations)
Protein–DNA and Cytoskeletal Regulation
– Elucidated post-translationally driven oligomerization of cofilin via disulfide bond forma tion, revealing key dimer–tetramer intermediates and their impact on actin filament binding in cytoskeletal remodeling. (JPCB 2024)
– Revealed how charge-driven DNA bending and multimeric Fis–DNA architectures jointly shape transcription factor binding specificity and intersegment transfer dynamics. (JACS 2022, JACS 2025)
– Developed kinetic models of Fis–DNA binding via multiscale simulations, challenging ther modynamic assumptions of gene regulation. (JACS 2016, JACS 2019, Protein Sci 2016; ∼165 citations)
• Large-scale Research Award, EMSL, Pacific Northwest National Laboratory, USA (2025)
• 2030 Cross-Generation Young Scholars Program, NSTC, Taiwan (2024–2027)
• Young Scholar Award, National Chung Cheng University (2025)
• Outstanding Research Award, National Chung Cheng University (2024)
• Research and Teaching Awards, Tamkang University (2019–2021)
• Dissertation Award–Excellence in Physical Chemistry, Chinese Chemical Society (2011)
—Associate Professor, National Chung Cheng University, Taiwan (2025.8–Present)
Department of Chemistry and Biochemistry
Core Member, NCTS– Nanoscale Physics & Chemistry Division
—Assistant Professor, National Chung Cheng University, Taiwan
Department of Chemistry and Biochemistry (2023.2–2025.7)
Core Member, NCTS– Nanoscale Physics & Chemistry Division
—Assistant Professor, Tamkang University, Taiwan (2018.8–2023.1)
—Postdoctoral Research Associate, Rice University, USA (2014.10–2018.7)
Center for Theoretical Biological Physics
Advisor: Prof. Peter G. Wolynes; Co-Advisor: Prof. Margaret S. Cheung (PNNL)
—Ph.D., Physical Chemistry, National Taiwan University, Taiwan (2011) Thesis: Theoretical Studies of Protein Folding Advisor: Prof. Sheng-Hsien Lin
—B.Sc., Chemistry, National Taiwan Normal University, Taiwan (2005) Research Mentor: Prof. Ying-Chieh Sun
Job Description
The intern will join a theoretical and computational biophysics laboratory focusing on multiscale molecular simulations and artificial intelligence applications in biomolecular systems. The project integrates:
Coarse-grained and all-atom molecular dynamics simulations
Energy landscape analysis of protein aggregation and membrane-associated processes
AI-assisted modeling, including protein language models (PLMs) and embedding-based structure-function analysis
Data-driven interpretation of diffusion dynamics and conformational transitions
The intern will participate in one of the following research themes:
- Protein aggregation and fibril surface diffusion
Investigating orientation-dependent diffusion, periodic free-energy modulation, and stochastic barrier-crossing dynamics on amyloid fibril surfaces. - AI-assisted sequence-to-dynamics modeling
Applying protein language models (e.g., ESM-style embeddings or transformer-based architectures) to correlate sequence representations with structural order parameters, frustration metrics, or diffusion coefficients. - Membrane–protein interaction modeling
Studying cooperative effects in protein–lipid systems (e.g., GM1-associated binding) through free energy analysis and statistical thermodynamics frameworks.
The intern will gain hands-on experience in:
Python-based scientific computing (NumPy, MDAnalysis, PyTorch)
Molecular simulation workflows (OpenMM, AWSEM-type models, GROMACS or NAMD familiarity is a plus)
Data visualization and interpretation of high-dimensional biomolecular data
Integrating AI representations with physically interpretable observables
The internship will emphasize both computational rigor and physical interpretability, bridging statistical thermodynamics with modern machine learning approaches.
Preferred Intern Educational Level
Senior undergraduate or Master’s students in Chemistry, Physics, Computational Biology, Chemical Engineering, Applied Mathematics, or related quantitative fields.
Skill sets or Qualities
- Programming experience (Python preferred)
- Basic knowledge of molecular simulations or machine learning
- Strong analytical thinking and quantitative background
- Ability to read scientific literature in English
- Curiosity about bridging physics-based models with AI approaches