DeepEnzyme

Turnover numbers (<i>k</i>_cat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes’ <i>k</i>_cat is always time-consuming. Recently, the prediction of <i>k</i>_cat using deep learning models has mitigated this problem. However, the accuracy and robustness in <i>k</i>_catprediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D structures. Consequently, our model exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes’ <i>k</i>_cat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

催化周转数（k_cat）用于表征酶的催化效率，在蛋白质工程、合成生物学等领域拥有广泛应用。通过实验测定酶的k_cat往往耗时良久。近年来，借助深度学习模型预测k_cat的方法缓解了这一难题，但当前k_cat预测的准确性与鲁棒性仍需大幅提升，尤其是在处理与训练数据集内酶序列相似性较低的酶样本时。
本文提出了DeepEnzyme——一款融合了当前最先进的Transformer与图卷积网络（Graph Convolutional Network, GCN）的前沿深度学习模型，可同时捕获蛋白质的序列信息与三维结构信息。为提升预测精度，DeepEnzyme依托序列与三维结构的整合特征开展训练。得益于高质量蛋白质三维结构提供的额外特征，本模型在处理与训练数据集内酶序列相似性较低的酶样本时，展现出优异的鲁棒性。
此外，DeepEnzyme还可用于评估点突变对酶催化活性的影响，有助于识别对催化功能至关重要的残基位点。
综上，相较于既往算法，DeepEnzyme在酶k_cat数值预测任务中实现了精度与鲁棒性的双重提升，是该领域的一项开创性工作。这一进展将极大推动我们对酶功能及其跨物种进化模式的理解。

搜集汇总

背景与挑战

背景概述

DeepEnzyme是一个先进的深度学习模型，用于预测酶的催化效率（kcat），通过整合酶的序列和3D结构信息，显著提高了预测的准确性和鲁棒性，尤其适用于序列相似性低的酶。该模型还能分析点突变对酶活性的影响，有助于理解酶的功能和进化模式。