Turnover numbers (kcat), 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' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction 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, DeepEnzyme 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' kcat 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.

Spatial–temporal data is widely available in intelligent transportation systems, and accurately solving non-stationary of spatial–temporal regression is critical. In most traffic flow prediction research, the non-stationary solution of deep spatial–temporal regression tasks is typically formulated as a spatial–temporal graph modeling problem. However, there are several issues: (1) the coupled spatial–temporal regression approach renders it unfeasible to accurately learn the dependencies of diverse modalities; (2) the intricate stacking design of deep spatial–temporal network modules limits the interpretation and migration capability; (3) the ability to model dynamic spatial–temporal relationships is inadequate. To tackle the challenges mentioned above, we propose a novel unified spatial–temporal regression framework named Generalized Spatial–Temporal Regression Graph Convolutional Transformer (GSTRGCT) that extends panel model in spatial econometrics and combines it with deep neural networks to effectively model non-stationary relationships of spatial–temporal regression. Considering the coupling of existing deep spatial–temporal networks, we introduce the tensor decomposition to explicitly decompose the panel model into a tensor product of spatial regression on the spatial hyper-plane and temporal regression on the temporal hyper-plane. On the spatial hyper-plane, we present dynamic adaptive spatial weight network (DASWNN) to capture the global and local spatial correlations. Specifically, DASWNN adopts spatial weight neural network (SWNN) to learn the semantic global spatial correlation and dynamically adjusts the local changing spatial correlation by multiplying between spatial nodes embedding. On the temporal hyper-plane, we introduce the Auto-Correlation attention mechanism to capture the period-based temporal dependence. Extensive experiments on the two real-world traffic datasets show that GSTRGCT consistently outperforms other competitive methods with an average of 62% and 59% on predictive performance.