Khan Mohd Faheem, Khan Mohd Tasleem
UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, D04 V1W8 Dublin, Ireland.
School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh Campus, Edinburgh EH14 4AS, UK.
Molecules. 2025 Dec 22;31(1):45. doi: 10.3390/molecules31010045.
Enzyme engineering drives innovation in biotechnology, medicine, and industry, yet conventional approaches remain limited by labour-intensive workflows, high costs, and narrow sequence diversity. Artificial intelligence (AI) is revolutionising this field by enabling rapid, precise, and data-driven enzyme design. Machine learning and deep learning models such as AlphaFold2, RoseTTAFold, ProGen, and ESM-2 accurately predict enzyme structure, stability, and catalytic function, facilitating rational mutagenesis and optimisation. Generative models, including ProteinGAN and variational autoencoders, enable de novo sequence creation with customised activity, while reinforcement learning enhances mutation selection and functional prediction. Hybrid AI-experimental workflows combine predictive modelling with high-throughput screening, accelerating discovery and reducing experimental demand. These strategies have led to the development of synthetic "synzymes" capable of catalysing non-natural reactions, broadening applications in pharmaceuticals, biofuels, and environmental remediation. The integration of AI-based retrosynthesis and pathway modelling further advances metabolic and process optimisation. Together, these innovations signify a shift from empirical, trial-and-error methods to predictive, computationally guided design. The novelty of this work lies in presenting a unified synthesis of emerging AI methodologies that collectively define the next generation of enzyme engineering, enabling the creation of sustainable, efficient, and functionally versatile biocatalysts.
酶工程推动了生物技术、医学和工业领域的创新，但传统方法仍然受到劳动密集型工作流程、高成本和狭窄序列多样性的限制。人工智能（AI）正在通过实现快速、精确且数据驱动的酶设计，给这一领域带来变革。诸如AlphaFold2、RoseTTAFold、ProGen和ESM-2等机器学习和深度学习模型能够准确预测酶的结构、稳定性和催化功能，有助于进行合理的诱变和优化。包括ProteinGAN和变分自编码器在内的生成模型能够创建具有定制活性的全新序列，而强化学习则增强了突变选择和功能预测。人工智能与实验相结合的工作流程将预测建模与高通量筛选相结合，加速了发现过程并减少了实验需求。这些策略推动了能够催化非天然反应的合成“合成酶”的开发，拓宽了其在制药、生物燃料和环境修复方面的应用。基于人工智能的逆合成和途径建模的整合进一步推动了代谢和过程优化。总之，这些创新标志着从经验性的试错方法向预测性的、计算引导设计的转变。这项工作的新颖之处在于对新兴人工智能方法进行了统一整合，这些方法共同定义了下一代酶工程，能够创造出可持续、高效且功能多样的生物催化剂。
