Artificial intelligence tools for enzyme engineering and metabolic engineering

Area of Science:

• Biotechnology
• Biochemical Engineering
• Synthetic Biology

Background:

• Enzyme engineering and metabolic engineering are key to advancing energy biotechnology.
• Artificial intelligence (AI) has emerged as a powerful tool in designing enzymes and microbial cell factories.
• Recent progress highlights AI's role in optimizing biological systems for energy applications.

Purpose of the Study:

• To review recent advancements in AI-driven enzyme redesign and metabolic engineering.
• To explore the application of AI in creating novel enzymes and improving microbial productivity.
• To discuss the integration of AI models across different biological scales.

Main Methods:

• Utilizing protein language models for enzyme redesign.
• Employing generative models for de novo enzyme design.
• Applying AI tools for engineering metabolic pathways and cellular phenotypes.
• Integrating diverse data representations for AI model development.

Main Results:

• AI models are increasingly successful in designing effective enzymes.
• AI tools are enhancing the productivity of microbial cell factories.
• AI is enabling the engineering of complex metabolic networks and cellular functions.
• A shift towards integrated AI models that consider multiple data modalities is observed.

Conclusions:

• AI is a transformative technology in energy biotechnology, accelerating innovation in enzyme and metabolic engineering.
• Integrating diverse data representations across scales is crucial for future advancements in AI-driven biotechnology.
• The synergy between AI and biological engineering holds significant promise for sustainable energy solutions.