AI-enabled protein design facilitates future plant research and crop breeding

Area of Science:

• Plant Science
• Computational Biology
• Biotechnology

Background:

• Artificial intelligence (AI) and protein language models are transforming life sciences research.
• These AI tools are increasingly applied to protein structure prediction, function analysis, and design.
• Their adoption in plant science and crop breeding is emerging but largely in proof-of-concept stages.

Purpose of the Study:

• To review fundamental principles, models, and tools of AI in protein research.
• To explore potential applications of AI-driven protein engineering in plant research and crop breeding.
• To discuss challenges and opportunities for AI in plant biological systems.

Main Methods:

• Introduction to general principles and models for protein understanding and generation.
• Case studies illustrating AI applications in fundamental plant research (e.g., maize gene analysis).
• Presentation of AI-enabled protein engineering strategies (rational, semi-rational, refactoring, de novo design).

Main Results:

• AI tools facilitate structure-aware interpretation of mutation-function relationships for hypothesis generation.
• AI-driven protein engineering strategies can create artificial variants with improved or novel functions.
• Case studies demonstrate AI's potential to advance plant research and innovation in crop breeding.

Conclusions:

• AI, particularly protein language models, holds significant potential for advancing plant research and crop breeding.
• Effective application requires addressing challenges like limited protein structure data and plant system complexity.
• AI-driven bio-breeding is a promising future direction requiring further development and validation.