Cell Context and Variant Effects: Multiplexed Assays for Synthetic Protein Innovation

About This Grant

Project Summary In 1966, Francis Crick described proteins as one of biology's two "great polymer languages". Despite progress, achieving fluency in this language—full understanding and the ability to engineer synthetic proteins for precise control of biological systems—remains a foundational challenge. Synthetic proteins, exemplified by engineered gene editors and chimeric antigen receptors, hold immense potential for medicine. While computational biology has advanced protein science through predictions of structure, stability, and mutation effects on disease, engineering multi-domain synthetic proteins remains difficult due to complex fitness landscapes. This limits our ability to build sophisticated proteins that integrate multiple inputs to control cellular states. Current biophysics- based models struggle with large multidomain proteins, and designs are constrained by cellular contexts, such as folding and trafficking. To address these challenges, my lab developed a Multiplexed Assays of Variant Effect (MAVE) pipeline that combines engineering comprehensive synthetic protein variant libraries, analyzing their phenotypes in diverse cellular contexts, with data-driven models of protein structure and function. While this has enabled better synthetic proteins, including opto- and chemogenetic reagents and cell type specific gene delivery vectors, we need to make MAVE cell context aware to understand how cellular contexts impinge on molecular mechanisms that constrain synthetic protein design. Over the next five years, I will pursue this through: 1) studying how protein molecular traits like folding stability relate to their functions within different cells and tissues, providing insight into cell-type specific variation, 2) assessing multiple phenotypes to uncover structure-function relationships and inform engineering strategies, and 3) exploring diverse systems like ion channels and viral capsids while creating biomedical tools. Through collaborations, these approaches will generate discoveries for independent investigation.