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The researchers developed a new basic editing tool using AI-predicted clustering of protein structures

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June 27, 2023

(Nanowerk News) The AO Caixia group of the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences has pioneered the use of artificial intelligence (AI) assisted methods to discover novel deaminase proteins with unique functions through structural prediction and classification.

This approach has opened up a wide range of applications for the discovery and creation of desirable plant genetic traits.

The results are published in Cell (“Discovery of deaminase function by structure-based protein clustering”). AI-assisted structural prediction and alignment establishes new protein classifications and functional mining methods AI-assisted structural prediction and alignment established new protein classification and functional mining methods, which further enabled the discovery of single- and double-stranded cytidine deaminase sequences that show great potential as bespoke base editors for therapeutic or agricultural breeding applications. (Image: IGDB)

The discovery of new proteins and the exploitation of various engineered enzymes have contributed to the rapid advancement of biotechnology. Currently, attempts to mine new proteins generally rely on amino acid sequences, which cannot provide a strong link between protein structural information and function.

Base editing is a new precision genome editing technology that has the potential to revolutionize molecular plant breeding by introducing desirable traits into elite germplasm. The discovery of several deaminases has greatly expanded the base-editing capabilities of cytosine. Although traditional sequence-based efforts have identified many proteins for use as base editors, limitations in editing certain DNA sequences or species still remain.

Canonical efforts based solely on protein engineering and directed evolution have helped to diversify the nature of basic editing, but challenges remain. By predicting the structure of proteins within the deaminase protein family using AlphaFold2, the researchers grouped and analyzed the deaminases based on structural similarities. They identified five novel deaminase clusters with cytidine deamination activity in the context of a DNA base editor.

Using this approach, they next reclassified a group of cytidine deaminases, called SCP1.201 and previously thought to act on dsDNA, to deaminate primarily on ssDNA. Through subsequent protein profiling and engineering efforts, they developed a new suite of DNA base editors with extraordinary features. This deaminase exhibits properties such as higher efficiency, lower generation of off-target editing events, edits on a different selection order motif, and a much smaller size.

The researchers emphasize that development of the basic editor suite will enable future tailor-made applications for various therapeutic or agricultural breeding endeavors. 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 found a highly effective deaminase from this clade specific to soybean plants, a globally significant agricultural crop that previously exhibited poor editing by the cytosine base editor.

In general, the recent emergence of protein structure prediction using growing genomic databases will greatly accelerate the development of new biotechnology tools.

This study highlights an approach that uses only the cytidine deaminase superfamily to develop a range of novel technologies and uncover novel protein functions. This newly discovered deaminase, based on AI-assisted structural prediction, greatly expands the usefulness of basic editors for therapeutic and agricultural applications.

In addition, this research will be of broad interest to the larger research community in the fields of phylogenetics, metagenomics, protein engineering and evolution, genome editing, and plant breeding.



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