Yu-Shan Lin

Expert in peptide simulation and design, particularly cyclic peptides for targeting protein–protein interactions.

>15 years of experience in simulations of various peptide systems, including intrinsically disordered peptides, transmembrane proteins, glycopeptides, and cyclic peptides.

Tools used include molecular dynamics simulations, enhanced sampling methods, machine learning, structural bioinformatics, computational substitution scans, and docking. Experienced with GROMACS, PLUMED, Rosetta, Schrödinger, Fortran, Matlab, and Python.

Strong track record of productive collaborations with experimentalists. Recent collaborators include Joshua Kritzer (Tufts Chemistry), Krishna Kumar (Tufts Chemistry), Bradley Pentelute (MIT Chemistry), Matthew Shoulders (MIT Chemistry), Ratmir Derda (University of Alberta Chemistry), and Adam Duerfeldt (University of Oklahoma Chemistry). >20 joint publications in journals including eLife, Nat. Comm., J. Am. Chem. Soc., J. Biol. Chem., FASEB J., Biophys. J., and J. Phys. Chem. B.

Mentored postdoctoral scholars, graduate students, and undergraduate students who went on to have successful careers in both academia and industry, including Novo Nordisk, Schrödinger, OpenEye, Microsoft, and Amazon Lab126.

Current funding includes R01GM124160 from NIH/NIGMS and the Program for Machine Learning in the Chemical Sciences & Engineering from the Dreyfus Foundation.

See https://ase.tufts.edu/chemistry/lin/paperYSL.html for the full list of publications.