Researchers develop new basic editing tools using protein structure clustering predicted by artificial intelligence

AI-assisted structural predictions and alignments establish new protein classification and functional extraction method

image: AI-assisted structural predictions and alignments establish a new protein classification and functional extraction method, further enabling the discovery of a suite of single- and double-stranded cytidine deaminases that show great potential as base editor tailored for therapeutic or agricultural breeding applications.
visualization Moreover

Credit: IGDB

GAO Caixia’s group from 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 new protein deaminases with unique functions through prediction structure and classification.

This approach has opened up a number of applications for the discovery and creation of desired plant genetic traits.

The results have been published in Cell.

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

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

Canon efforts based solely on protein engineering and directed evolution have helped diversify basic editing properties, but challenges persist. By predicting the structures of proteins within the protein deaminase 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 DNA base editors.

Using this approach, they further reclassified a group of cytidine deaminases, called SCP1.201 and previously thought to act on dsDNA, to perform deamination primarily on ssDNA. Through subsequent engineering and protein profiling efforts, they developed a suite of novel DNA base editors with notable features. These deaminases exhibit properties such as higher efficiency, lower generation of off-target modification events, modification in several preferred sequence motifs, and much smaller sizes.

The researchers emphasized that developing a core editor suite would allow future applications to be tailored to various therapeutic or agricultural breeding endeavors. They developed the smallest specific single-stranded 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 specific clade for soybean plants, a globally important agricultural crop that previously exhibited little modification by cytosine base editors.

Overall, the recent advent of protein structure prediction using growing genomic databases will greatly accelerate the development of new bioengineering tools.

This study highlights an approach using only the cytidine deaminase superfamily to develop a suite of novel technologies and discover novel protein functions. These newly discovered deaminases, based on AI-assisted structural predictions, greatly expand the utility of base editors for agricultural and therapeutic applications.

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

The study was supported by the National Natural Science Foundation of China, the National Key Research and Development Program of China, and the Ministry of Agriculture and Rural Affairs of China, among others.


Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of the press releases posted on EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

#Researchers #develop #basic #editing #tools #protein #structure #clustering #predicted #artificial #intelligence
Image Source : www.eurekalert.org

Leave a Comment