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## Article Highlight | 9-Feb-2024

The article “Improving genome engineering with quantum biology and artificial intelligence” by DOE/US Department of Energy explores a novel method to enhance the accuracy of the CRISPR Cas9 gene editing tool. This method draws on quantum chemistry, artificial intelligence, and synthetic biology to improve the predictive capabilities of the CRISPR-Cas tool.

### The Science

CRISPR-Cas is a tool that enables targeted changes to an organismÔÇÖs DNA. However, due to large variations in genome structure and composition among different organisms, predictions of where DNA modifications will occur often fail. To address this issue, researchers used artificial intelligence to improve the tool’s predictive behavior. By incorporating a novel set of quantum chemical properties, derived from the principles of quantum mechanics to better understand molecular interactions, the accuracy of predicting genome engineering with CRISPR-Cas was improved.

### The Impact

This study used explainable-artificial intelligence (XAI) to identify new biological features, aiming to understand the design of guide RNA and its association with CRISPR-based genome edits. The results could potentially improve scientists’ ability to predict where genomic targets will occur in a genome more efficiently.

### Summary

The researchers aimed to improve the reliability of CRISPR-Cas tools by using explainable-artificial intelligence to uncover new relationships between guide RNA, an organismÔÇÖs DNA, and the activity of CRISPR-Cas tools. Using publicly accessible datasets, the researchers trained an explainable artificial intelligence model called iterative Random Forest to predict the efficiency of CRISPR-Cas9 in editing specific DNA sequences with a specific guide RNA. The study found that quantum chemical features significantly affected the prediction of guide RNA efficiency in both humans and bacteria. The importance of different quantum chemical properties or locations of interest varied with each species, emphasizing the need for further research in this field to improve the safety and reliability of CRISPR-Cas tools in non-model organisms.

### Funding

The research was supported by the Secure Ecosystem Engineering and Design project funded by the Genomic Science Program of the Department of Energy Office of Science, Office of Biological and Environmental Research program as part of the Secure Biosystems Design Science Focus Area. One of the researchers was supported by the Center for Bioenergy Innovation, a Department of Energy research center.

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