Discovery and design of potent cell surface display elements.

Abstract

Cell surface display (CSD) elements are a major class of bioengineering modules, but systematic rules linking CSD sequence to functional potency are lacking. Here we develop DeepSCan, a suite of deep learning and artificial intelligence models to systematically map CSD sequence-function relationships to reliably capture potent elements' conserved features and iteratively design and develop potent de novo CSD modules for mRNA antigen display. We experimentally quantify surface expression across >570 chimeric antigens, derive cell surface translocation strength labels for ~310 CSD elements and compile ~45 independent training datasets. We train three generations of DeepSCan models and evaluate their performance. Guided by these models, we computationally design 3,700 and experimentally validate approximately 120 generative CSDs, identifying 7 generative CSDs that match or exceed the cell surface translocation strength of the most potent naturally occurring CSDs. Enhanced surface displays are validated across multiple cell types and are functional in an antigen-specific CAR-T cytotoxicity assay.