Enhancing ML-based binder design with high-throughput screening: a comparison of mRNA and yeast display technologies

Abstract excerpt

<h4>ABSTRACT</h4> Recent advances in machine learning (ML)-based protein design methods have enabled the rapid in silico generation of large libraries of miniprotein binders with minimal manual input. While computational design capacity has scaled rapidly, experimental validation methods have lagged, creating a bottleneck in binder discovery pipelines. Here, we apply mRNA display to screen an ML-designed minipro...

Topics

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• Computational Biology
• Computer Simulation
• Glycosylation and Glycoproteins Research
• Molecular Dynamics Simulation
• Monoclonal and Polyclonal Antibodies Research
• Mutation
• Protein Binding
• Viral Infectious Diseases and Gene Expression in Insects

Identifiers and source

Literature Corpus work

b24b5a18-5646-5387-848a-2245acea8334

DOI

10.64898/2026.02.12.705611