Mutant-Initiated Structure-Guided Refinement Enables Second-Generation Compact IscB Genome Editors

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: 34% mean increase in editing efficiency

• Primary Outcome: Genome editing efficiency improvement

• Nnt Or Sensitivity: Up to 2.1-fold improvement at individual loci; 4.46-fold improvement in A-to-G base editing

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility for research applications; clinical translation requires further development Relevant to Australian gene therapy research programs; subject to TGA oversight for clinical applications; aligns with NHMRC gene technology guidelines Research laboratories developing genome editing therapies; potential future clinical applications

Abstract

Compact RNA-guided nucleases such as IscB represent an attractive foundation for next-generation genome editors, yet their application in mammalian cells has been constrained by suboptimal activity. Here, instead of re-engineering enzymes, we establish a mutant-initiated, structure-guided optimization strategy to generate second-generation high-activity IscB editors. Using AlphaFold3 to model the engineered IscB*-ωRNA-DNA complex, we reveal remodeling of the nucleic-acid-binding interface induced by activity-enhancing substitutions. Guided by this predicted structure, we perform a focused mutational scan and identify V367 as an activity hotspot. Saturation mutagenesis at this position yields a single substitution, V367Y (IscB*-Act), which increases mean editing efficiency by 34% and achieves up to 2.1-fold improvement across endogenous targets in mammalian cells. Importantly, the V367Y substitution is transferable to an IscB-based adenine base editor, elevating A-to-G conversion by 68% on average and up to 4.46-fold at individual loci without altering the intrinsic editing window. Targeted off-target profiling at loci suggests that V367Y does not substantially increase off-target indels or A-to-G conversion. Together, our work demonstrates a practical framework for second-generation refinement of compact genome editors, bridging deep-learning-enabled structural prediction with interpretable protein engineering, and expands the functional potential of miniature IscB systems for both nuclease and base editing applications.