Computation-Aided Protein Engineering for Targeted Therapeutic Delivery

Citation

Ding, Xiaozhe (2023) Computation-Aided Protein Engineering for Targeted Therapeutic Delivery. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/7n15-3076. https://resolver.caltech.edu/CaltechTHESIS:05052023-192721059

Abstract

My Ph.D. projects centered on using computational structural biology tools to develop protein engineering methods for targeted therapeutic delivery, emphasizing delivering molecules to the brain. In this thesis, I focus on three main projects. First, utilizing computational structural biology techniques, I investigate the molecular mechanism that enables engineered adeno-associated viral (AAV) capsids to cross the blood-brain barrier (BBB). I develop a pipeline to model the vast and dynamic complex between engineered AAV capsids and their BBB receptors. I also apply a tool, recently developed by myself and discussed in Chapter 3, to distinguish capsids that bind to different receptors. The findings of this study can lead to novel approaches for developing chemicals and biologicals that can penetrate the human brain (Chapter 2). Second, I describe the development of Automated Pairwise Peptide-Receptor AnalysIs for Screening Engineered proteins (APPRAISE). This computational pipeline predicts the receptor binding propensity of engineered proteins based on competitive modeling and physics-grounded analysis. I show that APPRAISE is capable of distinguishing between receptor-dependent and receptor-independent adeno-associated viral vectors and ranking various engineered proteins, such as miniproteins binding to the SARS-CoV-2 spike and nanobodies binding to a G-protein-coupled receptor. A top performer in an in silico screening using APPRAISE was validated experimentally (Chapter 3). Third, I show an example to engineer a genetically encoded transmitter indicator (GETI), which may eventually be a cargo delivered to the brain. The GETI has a novel scaffold based on bacterial repressors, a class of transcriptional regulators that are critical for bacteria to respond to environmental chemicals. I repurposed an antibiotic-sensing repressor protein to bind a neurotransmitter, melatonin, using machine-learning-guided directed evolution. A melatonin indicator was then created by integrating the repurposed receptor with a fluorescent protein. This engineering platform may be adapted to create bio-orthogonal GETIs for various neurotransmitters (Chapter 4).

Thesis Files