Evidence map›Paper›PMID 39980225›Full record

ArticleDatabase : the journal of biological databases and curation2025

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

Lei Gong, Fufeng Liu, Chuanxi Zhang, Yongfan Ming, Yulan Mou, ZhaoTing Yuan, Haiming Jiang, Bei Gao, Fuping Lu, Lujia Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Database : the journal of biological databases and curation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Lei GongSchool of Chemistry and Molecular Engineering, East China Normal University, No.500 Dongchuan Road, Minhang District, Shanghai, China, Shanghai 200062, China.ORCID 0009-0002-4072-3521
Fufeng LiuSchool of Biotechnology, Tianjin University of Science and Technology, No.9, 13th Street, Economic and Technological Development Zone, Binhai New Area, Tianjin 300457, China.
Chuanxi ZhangDepartment of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, No.800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Yongfan MingSchool of Automation, Central South University, No.932 Lushan South Road, Changsha City, Hunan Province, Changsha 418405, China.
Yulan MouSchool of Chemistry and Molecular Engineering, East China Normal University, No.500 Dongchuan Road, Minhang District, Shanghai, China, Shanghai 200062, China.
ZhaoTing YuanSchool of Chemistry and Molecular Engineering, East China Normal University, No.500 Dongchuan Road, Minhang District, Shanghai, China, Shanghai 200062, China.
Haiming JiangSchool of Life Science and Technology, Inner Mongolia University of Science and Technology, No.7 Alding Street, Kundulun District, Baotou City, Inner Mongolia, Baotou 014010, China.
Bei GaoSchool of Biotechnology, East China University of Science and Technology, No.130 Meilong Road, Xuhui District, Shanghai 200237, China.
Fuping LuSchool of Biotechnology, Tianjin University of Science and Technology, No.9, 13th Street, Economic and Technological Development Zone, Binhai New Area, Tianjin 300457, China.
Lujia ZhangSchool of Chemistry and Molecular Engineering, East China Normal University, No.500 Dongchuan Road, Minhang District, Shanghai, China, Shanghai 200062, China.

Funding

National Key Research and Development Program of China 2019YFA0905200Science and Technology Commission of Shanghai Municipality 23HC1400500Science and Technology Major Project of Inner Mongolia Autonomous Region of China 2022YFHH0026
6 · The paper itself

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/.

Indexed as

Artificial IntelligenceDatabases, ProteinEnzymesEnzymes

Identifiers

PMID39980225
PMCPMC11842304

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.