In recent years, computational prediction of drug-target interactions (DTIs) has become essential for drug discovery and repositioning. However, traditional experimental approaches for DTI identification are time-consuming and costly. To address this, many machine learning-based methods have been developed, yet most existing models neglect important information interaction between drugs and targets in drug-target pairs (DTPs) during drug-target interaction. In this study, we propose a novel cross-attention and intra-layer attention mechanism within a heterogeneous graph neural network (CAIHGNN) for DTI prediction. The cross-attention mechanism allows for dynamic learning of feature correlations between drugs and targets, while the intra-layer attention captures both explicit and implicit interactions within DTPs. Additionally, we introduce a drug-target pair correlation graph to exploit high-order interactions between DTPs. Extensive experiments on two biological heterogeneous datasets demonstrate the superior performance of our proposed method in accurately predicting DTIs. Furthermore, the model exhibits robust generalization in case study, showing promise for real-world drug discovery applications.

MOTIVATION: S-sulfhydration, a crucial post-translational protein modification, is pivotal in cellular recognition, signaling processes, and the development and progression of cardiovascular and neurological disorders, so identifying S-sulfhydration sites is crucial for studies in cell biology. Deep learning shows high efficiency and accuracy in identifying protein sites compared to traditional methods that often lack sensitivity and specificity in accurately locating nonsulfhydration sites. Therefore, we employ deep learning methods to tackle the challenge of pinpointing S-sulfhydration sites. RESULTS: In this work, we introduce a deep learning approach called Sul-BertGRU, designed specifically for predicting S-sulfhydration sites in proteins, which integrates multi-directional gated recurrent unit (GRU) and BERT. First, Sul-BertGRU proposes an information entropy-enhanced BERT (IE-BERT) to preprocess protein sequences and extract initial features. Subsequently, confidence learning is employed to eliminate potential S-sulfhydration samples from the nonsulfhydration samples and select reliable negative samples. Then, considering the directional nature of the modification process, protein sequences are categorized into left, right, and full sequences centered on cysteines. We build a multi-directional GRU to enhance the extraction of directional sequence features and model the details of the enzymatic reaction involved in S-sulfhydration. Ultimately, we apply a parallel multi-head self-attention mechanism alongside a convolutional neural network to deeply analyze sequence features that might be missed at a local level. Sul-BertGRU achieves sensitivity, specificity, precision, accuracy, Matthews correlation coefficient, and area under the curve scores of 85.82%, 68.24%, 74.80%, 77.44%, 55.13%, and 77.03%, respectively. Sul-BertGRU demonstrates exceptional performance and proves to be a reliable method for predicting protein S-sulfhydration sites. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/Severus0902/Sul-BertGRU/.