Andrew White of Rochester University will deliver a seminar entitled, "Computational Design of Peptide-Based Materials with Data-Driven Modeling." Hosted by Glen Hocky.
For more information about Andrew White, click here.
Abstract: Peptides are small proteins built from monomer units called amino acids. Peptides can be precisely synthesized using solid-phase peptide synthesis and their constituent amino acids can provide functional groups ranging from hydrogen bond-donors to aromatics. This flexibility and precise control give a wide range of potential applications including self-assembling antifouling surface coatings, antimicrobial therapeutics, hydrogel vaccines, and nucleating crystal structures. In this talk, I will present computational methods my group has used to design peptides for antifouling, antimicrobial, and self-assembly. Our approach is to use insight from nature through data-driven informatics methods and maximum entropy molecular simulation. Molecular simulation seeks to model the dynamics of peptides at the atomic level. Maximum entropy methods minimally modify molecular simulations to match experimental data. This enables better accuracy, which is critical for modeling self-assembly of peptides, which is a complex multiscale process. Broadly our goal in methods development is to combine physics-based simulation with modern machine learning methods to create interpretable and accurate models.
Short Bio: Prof. Andrew White received his PhD in chemical engineering from the University of Washington in 2013 under Prof. Shaoyi Jiang. His thesis topic was the understanding and design of non-fouling biomaterials. Prof. White did his post-doc with Prof. Gregory Voth at the University of Chicago as a Yen Fellow in the Institute for Biophysical Dynamics. While at University of Chicago, Prof. White developed new methods for combining molecular simulations with external data from experiments. He became an assistant professor at the University of Rochester in 2015 in chemical engineering. He has joint appointments in biophysics, materials science, chemistry, and data science. In 2018, Prof. White was awarded an NSF Career Award for multiscale modeling of peptide self-assembly. He and his group have authored 26 peer-reviewed publications. Prof. White enjoys running, snowboarding and graphics design. His artwork at the molecular scale has appeared at the Visualiseringscenter C museum in Sweden. Prof. White is currently on junior sabbatical at UCLA’s Institute for Pure and Applied Mathematics as a senior fellow on machine learning for physics and physics of machine learning.