AI-driven protein engineering: A new paradigm for plant trait design

Area of Science:

• Plant biotechnology and synthetic biology
• Computational biology and artificial intelligence
• Molecular biology and protein engineering

Background:

• Protein engineering is crucial for developing novel biological functions.
• Artificial intelligence (AI) has emerged as a powerful tool for precise protein design in plants.
• The integration of AI with protein structure prediction and generative modeling enhances synthetic protein development.

Purpose of the Study:

• To review the advancements in AI-driven protein design for plant systems.
• To highlight eight key application areas of engineered proteins in plant physiology and breeding.
• To propose a roadmap for translating AI-driven plant trait design into practical breeding strategies.

Main Methods:

• Tracing the evolution of protein design methodologies from rational to AI-driven approaches.
• Integrating protein structure prediction, generative sequence modeling, and function optimization.
• Analyzing AI's role in expanding sequence space and prioritizing candidate proteins for plant applications.

Main Results:

• AI facilitates the creation of synthetic proteins with improved specificity, stability, and activity in plants.
• Applications span enhanced disease and insect resistance, abiotic stress tolerance, nutrient use efficiency, and biosensing.
• AI-driven platforms offer improved candidate prioritization and sequence space exploration for trait design.

Conclusions:

• AI-driven protein engineering represents a paradigm shift in designing plant traits.
• Addressing current bottlenecks in AI model reliability and experimental validation is essential for translation.
• A staged roadmap is proposed to guide the development and implementation of AI-driven plant trait design in breeding programs.