Lei Gong, Fufeng Liu, Chuanxi Zhang, Yongfan Ming, Yulan Mou, ZhaoTing Yuan, Haiming Jiang, Bei Gao, Fuping Lu, Lujia Zhang
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引用次数: 0
Abstract
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/.
期刊介绍:
Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent. The archiving, curation, analysis and interpretation of all of these data are a challenge. Database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data.
Database: The Journal of Biological Databases and Curation provides an open access platform for the presentation of novel ideas in database research and biocuration, and aims to help strengthen the bridge between database developers, curators, and users.