Medina-Ortiz David, Khalifeh Ashkan, Anvari-Kazemabad Hoda, Davari Mehdi D
Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany; Departamento de Ingeniería En Computación, Universidad de Magallanes, Avenida Bulnes, 01855, Punta Arenas, Chile.; Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, Santiago, Chile.
Department of Mathematical and Physical Sciences, College of Arts and Sciences, University of Nizwa, Nizwa 616, Sultanate of Oman.
Biotechnol Adv. 2025 Mar-Apr;79:108495. doi: 10.1016/j.biotechadv.2024.108495. Epub 2024 Dec 5.
Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on data-driven strategies. However, the lack of interpretability and transparency in these models limits their trustworthiness and applicability in real-world scenarios. Explainable Artificial Intelligence addresses these challenges by providing insights into the decision-making processes of machine learning models, enhancing their reliability and interpretability. Explainable strategies has been successfully applied in various biotechnology fields, including drug discovery, genomics, and medicine, yet its application in protein engineering remains underexplored. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. This perspective work explores the principles and methodologies of explainable artificial intelligence, highlighting its relevance in biotechnology and its potential to enhance protein design. Additionally, three theoretical pipelines integrating predictive models with explainable strategies are proposed, focusing on their advantages, disadvantages, and technical requirements. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed.
通过定向进化和（半）理性设计进行蛋白质工程已成为优化和增强具有所需特性蛋白质的强大方法。人工智能方法的整合通过基于数据驱动策略开发预测模型，进一步加速了蛋白质工程过程。然而，这些模型缺乏可解释性和透明度，限制了它们在实际场景中的可信度和适用性。可解释人工智能通过深入了解机器学习模型的决策过程来应对这些挑战，提高其可靠性和可解释性。可解释策略已成功应用于包括药物发现、基因组学和医学在内的各种生物技术领域，但其在蛋白质工程中的应用仍未得到充分探索。在蛋白质工程中纳入可解释策略具有巨大潜力，因为它可以通过揭示预测模型的工作方式来指导蛋白质设计，有利于机器学习辅助定向进化等方法。这项前瞻性工作探索了可解释人工智能的原理和方法，强调了其在生物技术中的相关性以及增强蛋白质设计的潜力。此外，还提出了将预测模型与可解释策略相结合的三种理论途径，重点介绍了它们的优缺点和技术要求。最后，讨论了可解释人工智能在蛋白质工程中仍然存在的挑战以及作为传统蛋白质工程方法支持工具的未来发展方向。
