PEZy-miner: An artificial intelligence driven approach for the discovery of plastic-degrading enzyme candidates

IF 3.7 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Renjing Jiang , Zhenrui Yue , Lanyu Shang , Dong Wang , Na Wei
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引用次数: 0

Abstract

Plastic waste has caused a global environmental crisis. Biocatalytic depolymerization mediated by enzymes has emerged as an efficient and sustainable alternative for plastic treatment and recycling. However, it is challenging and time-consuming to discover novel plastic-degrading enzymes using conventional cultivation-based or omics methods. There is a growing interest in developing effective computational methods to identify new enzymes with desirable plastic degradation functionalities by exploring the ever-increasing databases of protein sequences. In this study, we designed an innovative machine learning-based framework, named PEZy-Miner, to mine for enzymes with high potential in degrading plastics of interest. Two datasets integrating information from experimentally verified enzymes and homologs with unknown plastic-degrading activity were created respectively, covering eleven types of plastic substrates. Protein language models and binary classification models were developed to predict enzymatic degradation of plastics along with confidence and uncertainty estimation. PEZy-Miner exhibited high prediction accuracy and stability when validated on experimentally verified enzymes. Furthermore, by masking the experimentally verified enzymes and blending them into homolog dataset, PEZy-Miner effectively concentrated the experimentally verified entries by 14∼30 times while shortlisting promising plastic-degrading enzyme candidates. We applied PEZy-Miner to 0.1 million putative sequences, out of which 27 new sequences were identified with high confidence. This study provided a new computational tool for mining and recommending promising new plastic-degrading enzymes.

PEZy-miner:发现候选塑料降解酶的人工智能驱动方法
塑料废物已引发全球环境危机。由酶介导的生物催化解聚已成为塑料处理和回收的一种高效、可持续的替代方法。然而,使用传统的基于培养或 omics 方法发现新型塑料降解酶具有挑战性且耗时。开发有效的计算方法,通过探索不断增加的蛋白质序列数据库来识别具有理想塑料降解功能的新酶,引起了越来越多的兴趣。在这项研究中,我们设计了一个基于机器学习的创新框架,名为 PEZy-Miner,用于挖掘在降解相关塑料方面具有高潜力的酶。我们创建了两个数据集,分别整合了实验验证的酶和具有未知塑料降解活性的同源物的信息,涵盖了 11 种塑料底物。开发了蛋白质语言模型和二元分类模型来预测塑料的酶降解以及置信度和不确定性估计。PEZy-Miner 在经过实验验证的酶上表现出了很高的预测准确性和稳定性。此外,通过屏蔽实验验证的酶并将其混合到同源数据集中,PEZy-Miner 有效地将实验验证的条目集中了 14∼30 倍,同时筛选出了有潜力的塑料降解酶候选者。我们将 PEZy-Miner 应用于 10 万个推定序列,其中有 27 个新序列被鉴定为高置信度序列。这项研究为挖掘和推荐有潜力的新型塑料降解酶提供了一种新的计算工具。
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来源期刊
Metabolic Engineering Communications
Metabolic Engineering Communications Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
13.30
自引率
1.90%
发文量
22
审稿时长
18 weeks
期刊介绍: Metabolic Engineering Communications, a companion title to Metabolic Engineering (MBE), is devoted to publishing original research in the areas of metabolic engineering, synthetic biology, computational biology and systems biology for problems related to metabolism and the engineering of metabolism for the production of fuels, chemicals, and pharmaceuticals. The journal will carry articles on the design, construction, and analysis of biological systems ranging from pathway components to biological complexes and genomes (including genomic, analytical and bioinformatics methods) in suitable host cells to allow them to produce novel compounds of industrial and medical interest. Demonstrations of regulatory designs and synthetic circuits that alter the performance of biochemical pathways and cellular processes will also be presented. Metabolic Engineering Communications complements MBE by publishing articles that are either shorter than those published in the full journal, or which describe key elements of larger metabolic engineering efforts.
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