TY - JOUR
T1 - LICEDB
T2 - light industrial core enzyme database for industrial applications and AI enzyme design
AU - Gong, Lei
AU - Liu, Fufeng
AU - Zhang, Chuanxi
AU - Ming, Yongfan
AU - Mou, Yulan
AU - Yuan, Zhao Ting
AU - Jiang, Haiming
AU - Gao, Bei
AU - Lu, Fuping
AU - Zhang, Lujia
N1 - Publisher Copyright:
© 2025 The Author(s).
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85219021541
U2 - 10.1093/database/baaf001
DO - 10.1093/database/baaf001
M3 - 文章
C2 - 39980225
AN - SCOPUS:85219021541
SN - 1758-0463
VL - 2025
JO - Database : the journal of biological databases and curation
JF - Database : the journal of biological databases and curation
M1 - baaf001
ER -