Intrinsically disordered regions of proteins play a crucial role in cell signaling and drug discovery. However, their high structural flexibility makes accurate residue-level prediction challenging. Existing methods often rely on single-view representations or rigid manual fusion strategies, which fail to effectively balance the complex interplay between local amino acid preferences and long-range sequence patterns. To address these limitations, we propose D2MOE, a Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm, which consists of two stages. First, a dual-view multiscale feature extraction method is introduced. This method integrates evolutionary views with deep semantic views and employs multiscale extractors to capture structural information across diverse receptive fields. Second, a multi-objective evolutionary algorithm is designed to adaptively discover optimal fusion architectures. By co-evolving discrete feature selection and continuous fusion weights, the algorithm adaptively explores optimal cross-feature architectures to enhance predictive accuracy while maintaining model compactness. Experimental results across three benchmark datasets demonstrate that D2MOE consistently outperforms state-of-the-art methods. D2MOE combines the feature extraction capabilities of deep learning with the global search advantages of evolutionary algorithms, enabling efficient feature integration without manual design, and providing a robust computational tool for protein disorder prediction.

翻译：蛋白质的内在无序区域在细胞信号传导与药物发现中起着至关重要的作用。然而，其高度结构柔性使得精确的残基水平预测极具挑战性。现有方法通常依赖单视图表征或僵化的手动融合策略，难以有效平衡局部氨基酸偏好与长程序列模式之间复杂的相互作用。为应对这些局限，我们提出了D2MOE——一种基于双视图多尺度特征与多目标进化算法的方法，其包含两个阶段。首先，引入一种双视图多尺度特征提取方法。该方法将进化视图与深度语义视图相融合，并采用多尺度提取器以捕获不同感受野下的结构信息。其次，设计了一种多目标进化算法，以自适应地发现最优融合架构。通过协同演化离散特征选择与连续融合权重，该算法自适应地探索最优跨特征架构，从而在保持模型紧凑性的同时提升预测精度。在三个基准数据集上的实验结果表明，D2MOE始终优于现有最先进方法。D2MOE结合了深度学习的特征提取能力与进化算法的全局搜索优势，无需人工设计即可实现高效特征融合，为蛋白质无序区域预测提供了一个鲁棒的计算工具。