Effective therapeutic delivery requires navigating some of biology’s most formidable molecular barriers - many of which are decorated or structured by glycans. My group develops computational and data-driven methods to understand and design around these glycan-mediated challenges, spanning from viral immunology to therapeutic packaging and mucosal biology. At the viral interface, we have built a suite of modeling tools to characterize the dense glycan shields of pathogens such as HIV and SARS-CoV-2. These simulations quantify how glycans mask protein surfaces and identify vulnerable sites that antibodies can exploit, offering guidance for immunogen design. Building on this, we integrate machine learning with molecular modeling to predict antibody–antigen binding interfaces, advancing both antibody engineering and our broader ability to design therapeutics that can selectively target glycosylated proteins. Beyond immunology, we apply these principles to therapeutic delivery. In collaboration with industry partners, we design glycopeptide-based polymers as bespoke packaging for nucleic acid therapies. By leveraging glycans and peptide motifs, these vehicles are engineered to penetrate mucus barriers, protect payloads, and enhance uptake efficiency. Complementing this, we use atomistic simulations of mucins, the heavily glycosylated polymers that form mucus hydrogels, to model drug–mucus interactions and understand how these networks regulate transport. Together, these efforts highlight how computation and data science can transform glycans from obstacles into design elements, enabling more effective strategies for drug delivery, gene therapy, and biomaterial innovation.
