Gong Lei, Liu Fufeng, Zhang Chuanxi, Ming Yongfan, Mou Yulan, Yuan ZhaoTing, Jiang Haiming, Gao Bei, Lu Fuping, Zhang Lujia
School of Chemistry and Molecular Engineering, East China Normal University, No.500 Dongchuan Road, Minhang District, Shanghai, China, Shanghai 200062, China.
School of Biotechnology, Tianjin University of Science and Technology, No.9, 13th Street, Economic and Technological Development Zone, Binhai New Area, Tianjin 300457, China.
Database (Oxford). 2025 Feb 19;2025. doi: 10.1093/database/baaf001.
Enzymes, serving as eco-friendly catalysts, are progressively supplanting traditional chemical catalysts in light industry sectors such as feed, papermaking, textiles, detergents, leather, and sugar production. Despite this advancement, the variability in the performance of natural enzymes and the fragmentation and diversity of existing data formats pose significant challenges to researchers. Furthermore, AI-driven enzyme design is limited by the quality and quantity of available data. To address these issues, we introduce the light industrial core enzyme database (LICEDB), the first database dedicated exclusively to managing and standardizing enzymes for light industry applications. LICEDB, with its integrated modules for data retrieval, similarity analysis, and structural analysis, will enhance the efficient industrial application of enzymes and strengthen AI-driven predictive research, thereby advancing data sharing and utilization in the field of enzyme innovation. Database URL: http://lujialab.org.cn/on-line-databases/.
酶作为环境友好型催化剂，正在逐步取代饲料、造纸、纺织、洗涤剂、皮革和制糖等轻工业领域中的传统化学催化剂。尽管取得了这一进展，但天然酶性能的变异性以及现有数据格式的碎片化和多样性给研究人员带来了重大挑战。此外，人工智能驱动的酶设计受到可用数据质量和数量的限制。为了解决这些问题，我们引入了轻工业核心酶数据库（LICEDB），这是首个专门用于管理和规范轻工业应用酶的数据库。LICEDB具有数据检索、相似性分析和结构分析的集成模块，将提高酶的高效工业应用，并加强人工智能驱动的预测研究从而推动酶创新领域的数据共享和利用。数据库网址：http://lujialab.org.cn/on-line-databases/
