Designing nanopatterned surfaces using multi-modal ligand self-assembly
Tunable, nanoscale chemical patterns can be formed by interactions between neighboring ligands immobilized on a flat surface. Through developing an understanding of how ligand structure can be used to modulate the self-assembly process, we can design new ligands capable of assembling into a wide variety of complex patterns.
Applying graph neural networks to predict peptide properties
While synthetic peptides can solve problems ranging from treating antibiotic-resistant diseases to catalyzing new reactions, resource and time costs make experimentally exploring their massive design space intractable. We use a hierarchical graph approach to representing peptides in neural networks to predict properties including anti-microbial activity and chromatographic retention time.
Elucidating Biomolecular Surface Hydrophobicity
The project aims to establish design rules for peptide-functionalized surfaces, focusing on hydrophobicity as the primary objective, using Molecular Dynamics (MD) simulations and Deep Learning. These insights will aid in creating nanomaterials with tunable hydrophobicity and targeted binding affinity for specific solutes.
Injectable Pentapeptide Hydrogels for Biomimetic Applications
This project aims to design and computationally model injectable pentapeptide hydrogels that mimic the mechanical and structural properties of soft biological tissues, such as the brain. These shear-thinning, self-healing materials are developed using all-atom molecular dynamics simulations to uncover the design rules governing peptide self-assembly, hydrophobic clustering, and aromatic π-π interactions. The ultimate goal is to establish a predictive framework for designing sequence-defined materials with tunable properties. Future work will expand into coarse-grained simulations and generative modeling with diffusion-based deep learning (e.g., DDPM) to accelerate the discovery of functional peptide sequences for a wide range of biomedical applications.