Researchers use machine learning modeling tools to improve zinc finger nuclease editing technology

Genome editing is making inroads into biomedical research and medicine. A Japanese research team is accelerating the pace and reducing the cost of zinc finger nuclease (ZFN) technology, a major gene editing tool, by employing biomolecular modeling tools.

In a study published in cutting edge scienceResearchers from Hiroshima University and Japan's National Institute of Advanced Industrial Science and Technology have demonstrated how a modular assembly system powered by machine learning can improve gene editing.

“Genome editing is a promising tool for treating genetic diseases in a variety of fields,” said Shota Katayama, associate professor at Hiroshima University's Genome Editing Innovation Center. “By improving the efficiency of gene editing techniques, we will be able to more precisely modify the genetic information of living cells.”

Along with CRISPR/Cas9 and TALEN, zinc finger nucleases are important tools in the field of genome editing. Designed to cleave specific bonds within the polynucleotide strands of DNA molecules, these chimeric proteins consist of two fused domains: a DNA-binding domain and a DNA-cleavage domain. The zinc finger (ZF) protein-binding domain recognizes target DNA sequences within the complete genome, while the cleavage domain involves a specialized DNA-cutting enzyme called ND1 endonuclease.

ZFNs have several advantages over CRISPR/Cas9 and TALENs. First, unlike CRISPR-Cas9, ZFN's patent has already expired, making it impossible to pay high royalties for industrial use. Second, because ZFNs are smaller, ZFN-encoding DNA can be easily packaged into viral vectors with limited cargo space for in vivo and clinical applications.

To cut DNA, two ZFNs must join together. Therefore, they must be designed in pairs to work on new sites. However, constructing functional His ZFNs and improving their genome editing efficiency has proven difficult.

“Although we have made great progress in deriving zinc finger sets for new genomic targets, there is still room for improvement in our approach to design and selection,” Katayama said.

Although selection-based methods can also be used to construct assembled ZF proteins, these methods are labor-intensive and time-consuming. Another approach to construct assembled ZF proteins is the assembly of ZF modules using standard molecular biology techniques. This method provides researchers with a much easier way to construct assembled ZF proteins.

However, modularly assembled ZFNs have a small number of functional ZFN pairs, and the failure rate of tested ZFN pairs is 94%.

In the study, researchers at Hiroshima University and the National Institute of Advanced Industrial Science and Technology used publicly available resources in a modular assembly system to create more efficient and easily constructed zinc fingers for gene editing. aimed at creating a nuclease.

An important consideration in the design of ZFNs is the number of zinc fingers required for efficient and specific cleavage. The team hypothesized that modular assembly of ZF modules would be useful for constructing ZFNs with five or six fingers.

In their paper, the research team described how they used three biomolecular modeling tools, AlphaFold, Coot, and Rosetta, to improve the efficiency of constructing functional ZFNs and their genome editing efficiency.

Of the 10 ZFNs tested, the researchers obtained two feature pairs. Moreover, his engineering of ZFNs using AlphaFold, Coot, and Rosetta increased his genome editing efficiency by 5%, demonstrating the effectiveness of his engineering of ZFNs based on structural modeling.