A new, AI-based approach has been developed to discover novel deaminase proteins, used in base editing, through structural prediction and classification. The study highlights an approach that uses only the cytidine deaminase superfamily to develop a suite of new technologies and uncover new protein functions.

The researchers, led by Caixia Gao, PhD, at the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences, who focus on genome editing in plants, note that the development of a suite of base editors with unique functions—based on AI-assisted structural predictions—greatly expands the utility of base editors for therapeutic and agricultural applications—opening up a range of applications for the discovery and creation of desired plant genetic traits.

The results were published in Cell in the article, “Discovery of deaminase functions by structure-based protein clustering.

Currently, efforts to mine novel proteins generally rely on amino acid sequences, which cannot provide a robust link between protein structural information and function.

Base editing has the potential to revolutionize molecular crop breeding by introducing desired traits into elite germplasm. The discovery of several deaminases has expanded the capability of cytosine base editing. Although traditional sequence-based efforts have identified many proteins for use as base editors, limitations in editing specific DNA sequences or species still remain.

Canonical efforts based solely on protein engineering and directed evolution have helped to diversify base editing properties, but challenges persist. By predicting the structures of proteins within the deaminase protein family using AlphaFold2, the researchers clustered and analyzed deaminases based on structural similarities. They identified five new deaminase clusters with cytidine deamination activity in the context of DNA base editors.

In addition, the approach further reclassified a group of cytidine deaminases, SCP1.201, and previously thought to act on dsDNA, to perform deamination primarily on ssDNA. Through subsequent protein profiling and engineering efforts, they developed a suite of new DNA base editors with remarkable features. These deaminases exhibit properties such as higher efficiency, lower generation of off-target editing events, editing at different preferred sequence motifs, and much smaller size.

AI protein
AI-assisted structural predictions and alignments establishes a new protein classification and functional mining method, further enabling the discovery of a suite of single and double-strand cytidine deaminases which show great potential as bespoke base editors for therapeutic or agricultural breeding applications. [IGDB]
The researchers emphasized that the development of a suite of base editors would enable future tailor-made applications for various therapeutic or agricultural breeding efforts. They developed the smallest single-strand specific cytidine deaminase, enabling the first efficient cytosine base editor to be packaged in a single adeno-associated virus.

They also discovered a highly effective deaminase from this clade specifically for soybean plants, a globally significant agricultural crop that previously exhibited poor editing by cytosine base editors.

In general, the recent advent of protein structure prediction using growing genomic databases will greatly accelerate the development of new bioengineering tools. In addition, this study will be of broad interest to the larger research community in phylogenetics, metagenomics, protein engineering and evolution, genome editing, and plant breeding.

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