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LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design

  • Lei Gong
  • , Fufeng Liu
  • , Chuanxi Zhang
  • , Yongfan Ming
  • , Yulan Mou
  • , Zhao Ting Yuan
  • , Haiming Jiang
  • , Bei Gao
  • , Fuping Lu*
  • , Lujia Zhang*
  • *Corresponding author for this work
  • East China Normal University
  • Tianjin University of Science & Technology
  • Shanghai Jiao Tong University
  • School of Automation
  • Inner Mongolia University of Science and Technology
  • East China University of Science and Technology

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numberbaaf001
JournalDatabase : the journal of biological databases and curation
Volume2025
DOIs
StatePublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

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