Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the prediction of binding residues based on protein sequence information have exhibited notable advancements in predictive accuracy. Nevertheless, these methods continue to grapple with several formidable challenges, including limited means of extracting more discriminative features and inadequate algorithms for integrating protein and residue information. To address the problems, we propose ATP-Deep, a novel protein-ATP binding residues predictor. ATP-Deep harnesses the capabilities of unsupervised pre-trained language models and incorporates domain-specific evolutionary context information from homologous sequences. It further refines the embedding at the residue level through integration with corresponding protein-level information and employs a contextual-based co-attention mechanism to adeptly fuse multiple sources of features. The performance evaluation results on the benchmark datasets reveal that ATP-Deep achieves an AUC of 0.954 and 0.951, respectively, surpassing the performance of the state-of-the-art model. These findings underscore the effectiveness of assimilating protein-level information and deploying a contextual-based co-attention mechanism grounded in context to bolster the prediction performance of protein-ATP binding residues.

中文翻译：

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通过基于上下文的共同关注网络使用多视图特征学习预测蛋白质-ATP 结合残基

准确预测蛋白质-ATP 结合残基对于蛋白质功能注释和药物发现至关重要。致力于基于蛋白质序列信息预测结合残基的计算方法在预测准确性方面表现出了显着的进步。尽管如此，这些方法仍然面临着一些艰巨的挑战，包括提取更具辨别力的特征的手段有限以及整合蛋白质和残留信息的算法不足。为了解决这些问题，我们提出了 ATP-Deep，一种新型的蛋白质-ATP 结合残基预测器。 ATP-Deep 利用无监督预训练语言模型的功能，并结合来自同源序列的特定领域的进化上下文信息。它通过与相应的蛋白质水平信息集成进一步细化残基水平的嵌入，并采用基于上下文的共同关注机制来巧妙地融合多个特征源。基准数据集上的性能评估结果显示，ATP-Deep 的 AUC 分别为 0.954 和 0.951，超过了最先进模型的性能。这些发现强调了同化蛋白质水平信息和部署基于上下文的共同注意机制的有效性，以增强蛋白质-ATP 结合残基的预测性能。