Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Zhang, Dachuan;Xing, Huadong;Liu, Dongliang;Han, Mengying;Cai, Pengli;...

通讯作者：

Tian, Ye;Wu, Aibo;Hu, QN

作者机构：

[Hu, QN; Tian, Yu; Lin, Huikang; Han, Mengying; Wu, Aibo; Liu, Dongliang; Cai, Pengli; Le, Yingying; Zhang, Dachuan; Xing, Huadong; Hu, Qian-Nan; Tian, Ye] Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Computat Biol, CAS Key Lab Nutr Metab & Food Safety,Univ Chinese, Shanghai 200031, Peoples R China.

[Zhang, Dachuan] Swiss Fed Inst Technol, Inst Environm Engn, CH-8093 Zurich, Switzerland.

[Tian, Yu] Wuhan Polytech Univ, Sch Biol & Pharmaceut Engn, Wuhan 430023, Peoples R China.

[Sun, Bin; Guo, Yinghao] Harbin Med Univ, Dept Pharmacol, Harbin 150081, Peoples R China.

通讯机构：

[Hu, QN ; Tian, Y; Wu, AB] C

Chinese Acad Sci, Shanghai Inst Nutr & Hlth, CAS Key Lab Computat Biol, CAS Key Lab Nutr Metab & Food Safety,Univ Chinese, Shanghai 200031, Peoples R China.

语种：

英文

关键词：

biodegradation;synthetic biology;food safety;mycotoxin;machine learning;cheminformatics

期刊：

ACS CATALYSIS

ISSN：

2155-5435

年：

2024

卷：

期：

页码：

3336-3348

DOI：

10.1021/acscatal.3c04461

基金类别：

National Key Research and Development Program of China [2018YFA0900700, 2021YFC2103001, 2019YFA0904300, 2023YFF1104604]; National Science Fund for Distinguished Young Scholars [32025030]; Shanghai Agriculture Applied Technology Development Program, China [T2023219]; International Partnership Program of the Chinese Academy of Sciences (CAS) [153D31KYSB20170121, 176GJHZ2022031GC]; CAS Science and Technology Service Network Initiative Program [QYZDB-SSW-SMC012]

机构署名：

本校为其他机构

院系归属：

生命科学与技术学院

摘要：

Identifying functional enzymes for the catalysis of specific biochemical reactions is a major bottleneck in the de novo design of biosynthesis and biodegradation pathways. Conventional methods based on microbial screening and functional metagenomics require long verification periods and incur high experimental costs; recent data-driven methods apply only to a few common substrates. To enable rapid and high-throughput identification of enzymes for complex and less-studied substrates, we propose a robust enzyme's substrate promiscuity prediction model based on positive unlabeled learning. Using ...

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Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning

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