High-throughput cell-free profiling of SARS-CoV-2 RBD variants enables rapid and quantitative in vitro affinity landscape mapping

SARS-CoV-2 variants continue to threaten public health, necessitating the study of cumulative and epistatic effects of receptor-binding domain (RBD) mutations on antibody evasion. We present a high-throughput platform combining cell-free protein synthesis and microfluidics to quantify the affinity of a large number of RBD triplet mutants covering the evolutionary space between wild-type and Omicron against two therapeutic antibodies and one engineered binder. Using rapid in vitro gene assembly and cell-free synthesis, we expressed 518 RBD variants and obtained 31,740 quantitative affinity measurements to generate three comprehensive binding energy landscapes. This approach enables rapid and large-scale in vitro affinity profiling and machine learning-based predictions, providing a valuable tool for studying emerging variants.

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Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2025-09-23
DOI
https://doi.org/10.3929/ethz-c-000794387
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
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High-throughput cell-free profiling of SARS-CoV-2 RBD variants enables rapid and quantitative in vitro affinity landscape mapping

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Repository for Publications and Research Data (ETH Zurich)
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High-throughput cell-free profiling of SARS-CoV-2 RBD variants enables rapid and quantitative in vitro affinity landscape mapping

Fatemeh Arefi, Julia Tischler, Edward B. Irvine, NingNing Chen, Sai T. Reddy, Sebastian J. Maerkl, Laura Grasemann, Jiami Han, Maria Andrea Gonzales Castillo
article en

Abstract

SARS-CoV-2 variants continue to threaten public health, necessitating the study of cumulative and epistatic effects of receptor-binding domain (RBD) mutations on antibody evasion. We present a high-throughput platform combining cell-free protein synthesis and microfluidics to quantify the affinity of a large number of RBD triplet mutants covering the evolutionary space between wild-type and Omicron against two therapeutic antibodies and one engineered binder. Using rapid in vitro gene assembly and cell-free synthesis, we expressed 518 RBD variants and obtained 31,740 quantitative affinity measurements to generate three comprehensive binding energy landscapes. This approach enables rapid and large-scale in vitro affinity profiling and machine learning-based predictions, providing a valuable tool for studying emerging variants.

Repository for Publications and Research Data (ETH Zurich)
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SARS-CoV-2 and COVID-19 Research
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