.png){kind=icon}
返回
Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jcim.4c01801.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, remains a significant challenge. Compared with traditional experimental methods, computational approaches are much more resource-efficient and time-saving, but they often compromise on accuracy. To address this, we introduce the molecule-enzyme interaction (MEI) model, a novel machine learning framework designed to predict the probability that a given molecule is a substrate for a specified enzyme with high accuracy. Utilizing a comprehensive data set that encapsulates extensive information on enzymatic reactions and enzyme sequences, the MEI model seamlessly combines atomic environmental data with amino acid sequence features through an advanced attention mechanism within a hierarchical neural network. Empirical evaluations have confirmed that the MEI model outperforms the current state-of-the-art model by at least 6.7% in prediction accuracy and 8.5% in AUROC, underscoring its enhanced predictive capabilities. Additionally, the MEI model demonstrates remarkable generalization across data sets of varying qualities and sizes. This adaptability is further evidenced by its successful application in diverse areas, such as predicting interactions within the CYP450 enzyme family and achieving an outstanding accuracy of 90.5% in predicting the enzymatic breakdown of complex plastics within environmental applications. These examples illustrate the model's ability to effectively transfer knowledge from coarsely annotated enzyme databases to smaller, high-precision data sets, robustly modeling both sparse and high-quality databases. We believe that this versatility firmly establishes the MEI model as a foundational tool in enzyme research with immense potential to extend beyond its original scope.
Keyword:
PREDICTION
BIOCATALYSIS
TRANSFORMER
期刊
IF:
5.3
论文数:
9.1K
被引数:
4.0W
机构
引用论文
.png){kind=icon}
Machine learning-based prediction of enzyme substrate scope: Application to bacterial nitrilases
OAAI
OAAI分享收藏Machine learning approaches and databases for prediction of drug-target interaction: a survey paper用于预测药物-靶标相互作用的机器学习方法和数据库: 调查论文OAAI
OAAI分享收藏分享收藏
查看更多内容