Protein engineering study reveals deep learning designs tunable CcdB variants in Escherichia coli, indicating a generalizable approach for low-leakage counter-selection in synthetic biology.
The strong cytotoxicity of wild-type CcdB limits its use as a counter-selection marker, as leaky expression can cause unintended cell death. Here, we report the rational engineering of a tunable CcdB variant with minimal basal toxicity while retaining inducible lethality. Using a structure-aware deep learning-guided strategy, we prioritized mutations predicted to alter CcdB−DNA gyrase binding energetics and experimentally identified variants with reduced basal toxicity and retained inducible killing. Targeted validation identified CcdB_L96P, which significantly reduces basal toxicity while preserving effective killing upon induction. This variant exhibits minimal leakage and robust inducible lethality across multiple Escherichia coli strains without requiring specialized hosts. It enables antibiotic-free CRISPR gRNA plasmid construction with high accuracy and efficient enrichment of correctly assembled clones. In a succinic acid-producing chassis, CcdB_L96P imposes no detectable metabolic burden and supports effective removal of residual cells using low concentrations of anhydrotetracycline. Together, this work provides a generalizable protein-level strategy for tuning toxin activity in synthetic biology.
No takes yet. Share an insight, caveat, or question.
Li et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: