OpenABE Gene Editor Achieves Up to 36-Fold Efficiency Gain, Overcoming AI-Designed Editor Limitations
核心洞察
A joint Korean research team developed OpenABE (搜索), a next-generation adenine base editor achieving 16 to 36 times greater editing efficiency than prior AI-designed gene editors.
The editors were engineered using AlphaFold-based structural predictions and structure-guided protein engineering to overcome limitations of early AI-designed base editors.
OpenABE (搜索) 1.1 and 1.2 demonstrated performance comparable to ABE8e (搜索), the current gold-standard editor, while dramatically reducing off-target effects.
A joint research team led by Professor Daesik Kim of Sungkyunkwan University, along with collaborators from the University of Ulsan College of Medicine and Sungkyunkwan University School of Medicine, has unveiled 'OpenABE (搜索)' (Open Adenine Base Editor), a next-generation gene editing platform that boosts editing efficiency by up to 36 times compared to conventional AI-designed models. The breakthrough, achieved through structure-guided protein engineering, addresses critical shortcomings of early artificial intelligence-derived base editors and opens new therapeutic avenues for previously intractable genetic diseases (搜索).
Adenine base editors (ABEs) are a cornerstone of modern gene-editing technology, capable of locating and correcting specific adenine-to-guanine substitutions within the DNA sequence. While recent advances in artificial intelligence have enabled rapid design of novel gene editors not found in nature, these AI-generated models have been hampered by low editing efficiency and frequent off-target and bystander effects—unintended alterations to genetic letters outside the target site—limiting their clinical applicability.
Engineering Precision Through Structural Insight
Using AlphaFold-based predictions, Professor Kim's team conducted a meticulous three-dimensional structural analysis of base editors, identifying critical regions that enable the editor to firmly grasp target DNA. The researchers introduced customized mutations to these key regions and appended a specialized tail structure derived from the top-performing conventional base editor. This precise structural engineering yielded two next-generation editors: OpenABE (搜索) 1.1 and OpenABE 1.2.
The resulting editors demonstrated editing capabilities 16 to 36 times stronger than existing AI-designed gene editors, placing their performance on par with ABE8e (搜索)—widely regarded as the gold-standard gene editor in laboratories worldwide. Critically, the team also achieved a dramatic reduction in off-target effects surrounding target genes, delivering high precision that cleanly corrects only the intended genetic sequences.
Expanding the Reach of Base Editing
In a notable advance, the study demonstrated that precise editing is achievable not only for nuclear DNA but also for mitochondrial DNA, which has historically proven difficult to edit due to its unique structural properties. Furthermore, by encapsulating the OpenABE (搜索) editors into engineered virus-like particles (eVLPs) for cellular delivery, the team secured high safety standards that enable selective editing of only one or two target genes requiring therapeutic intervention.
"This study represents a landmark innovation where human scientists overcame the limitations of early AI-designed gene editors using structure-guided protein engineering," said Professor Daesik Kim. "By opening a path to safely cure the causes of genetic diseases (搜索) in both the nucleus and mitochondria, we expect this work to significantly accelerate the development of therapeutics for genetic disorders."
Addressing Off-Target Characterization Needs
The development of OpenABE (搜索) comes as the broader gene editing field increasingly recognizes the importance of comprehensive off-target characterization. Broken String Biosciences (搜索) recently announced the launch of BaseMap ABE, a platform built on the company's INDUCE-seq technology, designed to enable genome-wide off-target characterization specifically for adenine base editing applications. Unlike prediction-based approaches, BaseMap ABE generates genome-wide data directly from biologically relevant cells, providing researchers with clear understanding of editor specificity under physiologically relevant conditions. This platform supports guide optimization, editor selection, and preclinical decision-making for therapeutic programs adopting ABE technology.
The OpenABE (搜索) findings were published in a world-renowned international journal in genetics and molecular biology, drawing widespread attention from the global scientific community.