Introduction
Recent advances in AlphaFold-based structural prediction, generative protein design, and large-scale biochemical modeling have opened new possibilities for engineering catalysts previously considered too complex for rational redesign. Nitrogenase and related catalytic systems represent ideal targets because of their exceptional catalytic versatility, evolutionary importance, and potential applications in sustainable agriculture, green chemistry, renewable energy, and carbon-neutral biotechnology.
At the same time, the discovery of multiple nitrogenase-like enzyme families suggests that biological catalytic diversity remains vastly underexplored. AI-assisted methodologies now offer powerful tools to identify, reconstruct, and engineer these systems at unprecedented speed and precision.
This thematic issue will provide an interdisciplinary forum connecting structural biologists, computational scientists, chemists, synthetic biologists, and evolutionary researchers working toward next-generation catalytic systems inspired by nature.
Keywords
AI-Assisted Protein Engineering, AlphaFold Structural Prediction, Generative Protein Design, Nitrogenase Enzyme Systems, Biocatalyst Engineering, Synthetic Biology Catalysts, Computational Enzyme Modeling, Sustainable Catalytic Biotechnology
Sub-topics
- AI-guided nitrogenase engineering
- Nitrogenase-like enzyme discovery
- Deep learning for catalyst prediction
- Computational metalloenzyme design
- Synthetic carbon fixation systems
- Biological hydrogen production
- Light-driven catalytic pathways
- Oxygen tolerance mechanisms
- Evolutionary reconstruction of catalytic systems
- Sustainable bioinspired catalysis
- AI in synthetic biology
- Artificial organelles and programmable metabolism
