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.周转数（Turnover numbers，$k_{cat}$）是衡量酶催化效率的核心指标，在蛋白质工程、合成生物学等诸多研究领域均具备广泛应用价值。实验测定酶的$k_{cat}$往往耗时冗长，近年来借助深度学习模型预测$k_{cat}$的研究方向缓解了这一痛点，但当前$k_{cat}$预测的准确性与鲁棒性仍存在显著提升空间，尤其当处理与训练数据集内酶序列相似性较低的酶样本时，该问题尤为突出。在此，我们提出DeepEnzyme——一款融合当前最先进Transformer与图卷积网络（Graph Convolutional Network，GCN）的前沿深度学习模型，其可同时捕获蛋白质的序列与三维结构信息。为进一步提升预测精度，DeepEnzyme通过整合蛋白质序列与三维结构的多维度特征完成训练。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样本时，展现出优异的鲁棒性。此外，DeepEnzyme还可实现对单点突变如何影响酶催化活性的量化评估，有助于精准鉴定对酶催化功能至关重要的残基位点。综上，相较于此前的算法，DeepEnzyme在预测酶的$k_{cat}$值方面实现了准确性与鲁棒性的双重提升，属于该领域的开创性工作。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。