Majumder Debashrita, Lahiri Dibyajit, Nag Moupriya, Bhattacharya Debasmita, Roy Rupak, Paul Tania, Pandit Soumya
Department of Biotechnology, Institute of Engineering and Management Kolkata, University of Engineering and Management, Kolkata, India.
Department of Basic Science and Humanities, Institute of Engineering and Management Kolkata, University of Engineering and Management, Kolkata, India.
Arch Microbiol. 2025 Jun 4;207(7):166. doi: 10.1007/s00203-025-04361-9.
Lipases play a pivotal role in biocatalysis, particularly in industrial and pharmaceutical applications, due to their exceptional regio- and enantioselectivity. However, their inherent limitations, including low stability, substrate specificity constraints, and suboptimal catalytic efficiency, hinder broader utilization. Genetic modifications have emerged as a powerful strategy to enhance lipase performance, offering significant improvements in enzyme activity, thermal stability, and substrate adaptability. This study presents a comprehensive investigation into the molecular engineering of lipases, leveraging site-directed mutagenesis and computational modelling to optimize structural and functional attributes. Key advancements in protein engineering, including rational design and directed evolution, are explored to elucidate their impact on catalytic efficiency and industrial viability. Experimental validation confirms that the genetically modified lipases exhibit superior stability under extreme pH and temperature conditions, along with enhanced catalytic turnover rates. Comparative analyses with wild-type enzymes underscore the potential of engineered lipases in diverse biotechnological applications, ranging from biofuel synthesis to pharmaceutical drug development. Furthermore, the study examines the mechanistic insights underlying these modifications, offering a theoretical framework for future enzyme engineering efforts. The findings underscore the transformative potential of genetically enhanced lipases in industrial biotechnology, paving the way for more sustainable and cost-effective biocatalytic processes. Future research should focus on integrating machine learning and advanced computational tools to further refine enzyme optimization strategies.
脂肪酶在生物催化中发挥着关键作用，特别是在工业和制药应用中，这得益于它们卓越的区域选择性和对映体选择性。然而，它们固有的局限性，包括稳定性低、底物特异性限制和催化效率欠佳，阻碍了其更广泛的应用。基因改造已成为提高脂肪酶性能的有力策略，在酶活性、热稳定性和底物适应性方面有显著提升。本研究对脂肪酶的分子工程进行了全面调查，利用定点诱变和计算建模来优化结构和功能属性。探索了蛋白质工程的关键进展，包括理性设计和定向进化，以阐明它们对催化效率和工业可行性的影响。实验验证证实，基因改造后的脂肪酶在极端pH和温度条件下表现出卓越的稳定性，同时催化周转率也有所提高。与野生型酶的比较分析强调了工程脂肪酶在从生物燃料合成到药物开发等各种生物技术应用中的潜力。此外，该研究考察了这些修饰背后的机制见解，为未来的酶工程工作提供了理论框架。研究结果强调了基因增强脂肪酶在工业生物技术中的变革潜力，为更可持续和更具成本效益的生物催化过程铺平了道路。未来的研究应专注于整合机器学习和先进的计算工具，以进一步完善酶的优化策略。
