Yu-Shan Lin

Yu-Shan Lin is a Professor and Chair of the Chemistry Department at Tufts University. She leads the YSL Group, which focuses on computational chemistry approaches to understanding biomolecular structures and functions.

Education:

• PhD in Chemistry, University of Wisconsin–Madison, 2009
• MS in Chemistry, University of Wisconsin–Madison, 2007
• BS in Chemistry, National Taiwan University, 2004

Research Interests: Professor Lin's research focuses on Theoretical and Computational Biophysical Chemistry. Her lab integrates advanced computations with chemical theory to address biological problems difficult to study experimentally. Key research areas include developing computational methodologies for cyclic peptide structure prediction, designing cyclic peptides to modulate protein-protein interactions, and investigating how post-translational modifications and non-natural amino acids affect protein folding. Her group employs molecular dynamics simulations, enhanced sampling methods, and machine learning techniques to tackle these complex biomolecular problems.

Publication Trends: Professor Lin's recent publications demonstrate a strong focus on computational approaches to peptide structure prediction and protein interactions. Her work increasingly integrates machine learning with molecular dynamics simulations to efficiently predict cyclic peptide conformations. Her research shows a clear trajectory toward developing more sophisticated computational tools for biomolecular modeling, with applications in drug design and protein engineering.

Teaching & Mentoring: Professor Lin actively recruits graduate students for her research group, emphasizing that prior computational chemistry experience is not required as they provide training. Her lab holds regular group meetings (Fridays at 10 am in Summer 2025) and seeks students passionate about peptides and proteins who are comfortable with math, physics, and computers.

Research Group: The YSL Group operates within Tufts University's Chemistry Department, focusing on computational approaches to biomolecular problems. The lab maintains active research projects in cyclic peptide modeling, protein folding studies, and MD data analysis techniques. They are accepting graduate students but not currently taking high school or undergraduate researchers.