标题： ResCap-DBP：一种轻量级残差胶囊网络，用于使用全局ProteinBERT嵌入的准确DNA结合蛋白预测 显示英文标题

标题： ResCap-DBP: A Lightweight Residual-Capsule Network for Accurate DNA-Binding Protein Prediction Using Global ProteinBERT Embeddings

摘要： DNA结合蛋白（DBPs）在基因调控和细胞过程中起着关键作用，因此准确识别它们对于理解生物功能和疾病机制至关重要。 用于DBP识别的实验方法耗时且成本高，这推动了高效计算预测技术的需求。 在本研究中，我们提出了一种新的深度学习框架ResCap-DBP，该框架结合了基于残差学习的编码器和一维胶囊网络（1D-CapsNet），直接从原始蛋白质序列中预测DBPs。 我们的架构在残差块中引入扩张卷积以缓解梯度消失问题并提取丰富的序列特征，同时具有动态路由的胶囊层捕获学习特征空间内的层次和空间关系。 我们进行了全面的消融研究，比较了ProteinBERT的全局和局部嵌入与传统one-hot编码。 结果表明， ProteinBERT嵌入在大型数据集上显著优于其他表示方式。 尽管one-hot编码在较小的数据集如PDB186上表现出微弱优势，但其难以有效扩展。 在四对公开可用的基准数据集上的广泛评估表明，我们的模型始终优于当前最先进的方法。 它在PDB14189和PDB1075上的AUC分数分别为98.0%和89.5%。 在独立测试集PDB2272和PDB186上，模型达到了83.2%和83.3%的最高AUC，同时在较大的数据集如 PDB20000上保持了有竞争力的性能。 值得注意的是，该模型在不同数据集上保持了良好的灵敏度和特异性。 这些结果证明了将全局蛋白质表示与先进的深度学习架构相结合在多样化的基因组环境中进行可靠和可扩展的DBP预测的有效性和通用性。 显示英文摘要

摘要： DNA-binding proteins (DBPs) are integral to gene regulation and cellular processes, making their accurate identification essential for understanding biological functions and disease mechanisms. Experimental methods for DBP identification are time-consuming and costly, driving the need for efficient computational prediction techniques. In this study, we propose a novel deep learning framework, ResCap-DBP, that combines a residual learning-based encoder with a one-dimensional Capsule Network (1D-CapsNet) to predict DBPs directly from raw protein sequences. Our architecture incorporates dilated convolutions within residual blocks to mitigate vanishing gradient issues and extract rich sequence features, while capsule layers with dynamic routing capture hierarchical and spatial relationships within the learned feature space. We conducted comprehensive ablation studies comparing global and local embeddings from ProteinBERT and conventional one-hot encoding. Results show that ProteinBERT embeddings substantially outperform other representations on large datasets. Although one-hot encoding showed marginal advantages on smaller datasets, such as PDB186, it struggled to scale effectively. Extensive evaluations on four pairs of publicly available benchmark datasets demonstrate that our model consistently outperforms current state-of-the-art methods. It achieved AUC scores of 98.0% and 89.5% on PDB14189andPDB1075, respectively. On independent test sets PDB2272 and PDB186, the model attained top AUCs of 83.2% and 83.3%, while maintaining competitive performance on larger datasets such as PDB20000. Notably, the model maintains a well balanced sensitivity and specificity across datasets. These results demonstrate the efficacy and generalizability of integrating global protein representations with advanced deep learning architectures for reliable and scalable DBP prediction in diverse genomic contexts.