Artificial Intelligence-Assisted CRISPR/Cas Systems for Targeting Plant Viruses

Area of Science:

• Plant Pathology and Virology
• Genetics and Genomics
• Agricultural Biotechnology

Background:

• Plant viral infections significantly threaten global food security, exacerbated by climate change and intensive farming.
• CRISPR/Cas systems offer precise genome editing for virus resistance but face limitations in guide RNA design, off-target effects, and understanding plant-virus dynamics.

Purpose of the Study:

• To summarize advancements in integrating artificial intelligence (AI) into CRISPR-Cas systems for plant antiviral strategies.
• To explore AI's role in optimizing CRISPR design, improving target specificity, and enhancing validation for virus resistance in crops.

Main Methods:

• Review of AI methodologies, including machine learning and deep learning algorithms (CNNs, RNNs, Transformers, generative models).
• Application of AI for predicting protein structures (e.g., AlphaFold2, RoseTTAFold, ESMFold), scoring single-guide RNAs (sgRNAs), and modeling host-virus interactions.
• Focus on AI-driven improvements in in silico validation and CRISPR-Cas performance.

Main Results:

• AI-enhanced CRISPR methods demonstrate improved target specificity and Cas protein performance.
• AI facilitates more accurate prediction of protein structures and sgRNA efficacy.
• In silico validation is significantly improved through AI-driven modeling of host-virus interactions.

Conclusions:

• AI integration represents a significant leap forward for CRISPR-based antiviral strategies in plants.
• AI-powered genome editing holds promise for developing next-generation virus-resistant crops and advancing sustainable agriculture.