UC Berkeley

Virus-like particles (VLPs) are nanoscale proteinaceous materials that show promise as scaffolds for a plethora of applications, including vaccine development, targeted drug delivery, and nanoreactor production. These self-assembling structures are based on viral architectures, but lack genetic material to cause infection. Though theoretically particle features may be finely tailored via genetic manipulation, the phenotypic consequences of mutations to self-assembling proteins remains hard to predict. Throughout this work, advancements in both the functional engineering of VLPs and the fundamental understanding of VLP design constraints are described. A method for selection of chemically modifiable capsids was developed and applied to a systematic library of N-terminally extended bacteriophage MS2 VLPs, resulting in highly reactive capsids for site-specific bioconjugation. The one-dimensional fitness landscape of a non-native MS2 assembly configuration was also constructed and used to develop machine learning models for assembly state prediction. This work highlights the utility of fitness landscaping in producing materials with applications-driven properties and explores the possibilities of in silico modeling for targeted engineering of VLPs. Furthermore, efforts to assess and improve the equity of the Chemistry academic community for marginalized populations are discussed. An annual survey framework was developed for generating quantitative understanding of departmental climate over time, and longitudinal improvements in departmental inclusivity were observed. This method was also used to critically assess how academicvalues of underrepresented graduate students are not well represented in faculty modes of evaluation. The developed framework is readily adaptable to other institutions aiming to improve the inclusion of historically minoritized groups in STEM.