Aging Brain Seminar with Yu-Shan Lin, PhD, Tufts University

• Date: Friday, July 24, 2024
• Time: 4:00pm
• Location: Picower Seminar Room (46-3310, Third Fl of MIT Building 46)
• Host: Li-Huei Tsai
• Please note that this seminar is offered in person only.
• Reception to follow

Talk Title: Accelerating Structure-Based Design of Cyclic Peptide Binders and Inhibitors with Molecular Dynamics Simulations and Machine Learning

Abstract: Peptides occupy a unique chemical space between small molecules and proteins. Their tunable backbone topology and amino-acid chemistry, especially through the use of non-canonical amino acids beyond the DNA-encoded twenty, enable high-affinity recognition of shallow or extended protein surfaces while offering avenues to optimize specificity, stability, membrane permeability, and other drug-like properties. Small cyclic peptides (5–15 amino acids) are of particular interest because they can balance conformational control with adaptive flexibility, enabling strong binding and, in some cases, unexpectedly favorable passive membrane permeability and oral bioavailability. A major challenge in cyclic peptide development is the limited availability of structural information, which hinders structure-based design and makes it difficult to understand why different cyclic peptide sequences exhibit varying binding affinities, membrane permeabilities, and other properties. This lack of structural insight arises because most cyclic peptides exist as ensembles of multiple conformations in solution, making them extremely challenging to characterize experimentally using techniques such as solution NMR spectroscopy. In this talk, I will describe how molecular dynamics simulations with enhanced sampling methods can be used to obtain this critical structural information. I will also introduce StrEAMM (Structural Ensembles Achieved by Molecular dynamics and Machine learning), a framework that integrates simulation data with machine learning to rapidly generate high-quality structural predictions for cyclic peptides. This capability to efficiently predict structural ensembles will help researchers uncover the structural origins of cyclic peptide behavior and significantly accelerate the development of this promising class of molecules.

Bio: Yu-Shan Lin is a computational peptide chemist and currently a Professor of Chemistry and Dean of Academic Affairs at Tufts University. She received her Ph.D. in Chemistry from the University of Wisconsin, Madison, in 2009, and she was a postdoctoral fellow at Stanford from 2009 to 2012. In 2012, Dr. Lin established her research group at Tufts University, where she received tenure in 2018. Her current research endeavors focus on using molecular dynamics simulations and machine learning to understand and design the structures and properties of peptides, particularly cyclic and other restrained peptides. Dr. Lin recently received the Machine Learning in the Chemical Sciences & Engineering Award from the Camille and Henry Dreyfus Foundation (2020) and the Rising Innovator Award from Tufts University (2023).

About The Aging Brain Initiative

The Aging Brain Initiative is an interdisciplinary research effort that pulls together faculty expertise, knowledge, and technical resources from across MIT to solve the mysteries of the aging brain. It spans neuroscience, fundamental biology and genetics, investigative medicine, engineering and computer science, economics, chemistry, urban planning, and artificial intelligence to enable a comprehensive systems approach. What's the ultimate mission? To deliver the basic research that makes possible new tools to address the challenges of brain aging and create a better future for millions.

Read more here: https://picower.mit.edu/research/aging-brain-initiative