Exploring structure-function relationships in engineered receptor performance using computational structure prediction.

Abstract

Engineered receptors play increasingly important roles in transformative cell-based therapies. However, the structural mechanisms that drive differences in performance across receptor designs are often poorly understood. Recent advances in protein structural prediction tools have enabled the modeling of virtually any user-defined protein, but how these tools might build understanding of engineered receptors has yet to be fully explored. In this study, we employed structural modeling tools to perform post hoc analyses to investigate whether predicted structural features might explain observed functional variation. We selected a recently reported library of receptors derived from natural cytokine receptors as a case study, generated structural models, and from these predictions quantified a set of structural features that plausibly impact receptor performance. Encouragingly, for a subset of receptors, structural features explained considerable variation in performance, and trends were largely conserved across structurally diverse receptor sets. This work indicates potential for structure prediction-guided synthetic receptor engineering.