From Nature-Limited to AI-Designed: The Future of Gene Editing

The Limits of Nature in Gene Editing

Gene editing has moved well beyond proof of concept, now with several approved medicines and the technology expanding beyond the original CRISPR-Cas9 approach. Multiple modalities of gene editing tools now exist, each designed to address different classes of genetic disease. Yet these tools face a common limitation: they were built from proteins shaped by evolution, not engineered for human therapeutics.

Consider base editing. Enzymes called deaminases enable precise single-nucleotide changes, but naturally occurring variants are constrained by off-target activity and unwanted "bystander" edits at neighboring positions. These limitations are the result of proteins that evolved for biological functions unrelated to human therapeutics. Expanding beyond what nature provides requires a fundamentally different approach to protein design.

ElevateBio’s Generative AI Platform: From Discovery to Design

Generative AI is transforming protein engineering by moving the field from adapting nature to designing it. ElevateBio’s gene editing platform demonstrates this shift, not only by expanding its natural protein library, but generating entirely novel proteins – and then optimizing them for therapeutic relevance.

The foundation is ElevateBio’s proprietary catalog of more than 10 billion proteins extracted from extensive genome and metagenome collections. This dataset provides the necessary breadth to train generative protein language models (PLMs) that produce proteins beyond what natural evolution has explored. Once generated, AI can also help guide the development of these proteins into effective gene editors, establishing an end-to-end workflow for designing purpose-built therapeutic proteins.

Proof of Concept: AI-Designed Deaminases for Base Editing

ElevateBio demonstrated this approach by designing novel adenine deaminases for base editing. Using ElevateBio’s generative AI trained on more than 370,000 curated protein sequences from its 10+ billion protein catalog, the platform generated de novo proteins with low sequence identity to known deaminase. These were genuinely novel sequences not found in nature.

After a single round of machine-learning optimization, the top candidate showed a 20-fold improvement in on-target A-to-G editing efficiency. Critically, this potency came without sacrificing precision: the optimized editor maintained less than 1% bystander effects at the critical +2 position, just two nucleotides from the on-target site.

These results were validated across a panel of therapeutically relevant gene targets, demonstrating that the minimal bystander profile is a generalizable property of this AI-designed protein’s architecture. Importantly, this optimized editor enabled targeting of previously intractable genomic sites, highlighting how AI can expand not only performance, but the range of diseases that may ultimately be addressable through base editing.

Beyond Deaminases: AI Applicability Across Editing Modalities

The implications extend well beyond a single class of enzymes. ElevateBio is already applying the same generative AI and machine learning capabilities to design novel large serine recombinases (LSRs) for targeted gene insertion addressing diseases that require insertion of full-length genes rather than single-base corrections.

Active learning-guided optimization has already demonstrated 3.4-fold potency improvements on recombinase proteins in mammalian cells, validating that the same ML pipeline proven on deaminases translates to entirely different enzyme classes and editing mechanisms.

As ElevateBio’s datasets grow and its models improve, this AI-enabled optimization workflow will be applied across its full spectrum modalities – nucleases, base editors, RT editors, epigenetic modifiers, and targeted insertion systems – with properties designed for specific diseases and delivery methods. By beginning with AI-enabled diversity rather than relying solely on naturally occurring biology, ElevateBio can identify editing systems optimized for distinct disease mechanisms and patient needs, while also accelerating development timelines for partners.

Redefining the Boundaries of Therapeutic Development

Generative AI is redefining the boundaries of protein engineering and therapeutic design. By enabling the creation of proteins tailored for specific diseases, delivery strategies, and clinical constraints, AI-driven engineering shortens the path from concept to clinic and expands the range of treatable diseases.

The future of gene editing is no longer limited by what evolution has already produced, but by what biology can now be intentionally designed to achieve.