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 the 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
Cell Systems
Published
2026-09-01
DOI
https://doi.org/10.1016/j.cels.2026.101713
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
0.00

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

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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 the 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.

Cell Systems
ETH Zurich (CH), Institute of Molecular and Clinical Ophthalmology Basel (CH), École Polytechnique Fédérale de Lausanne (CH)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, European Research Council
Affordable and clean energy
Openalex Percentile: Top 50%
SARS-CoV-2 and COVID-19 Research
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