Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes. We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance. We present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.

中文翻译：

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LVPocket：通过蛋白质结构分类的迁移学习，将 3D 全局局部信息集成到蛋白质结合袋预测中


以往预测蛋白质结合袋的深度学习方法主要采用3D卷积，但大量的卷积运算可能导致模型过度优先考虑局部信息，从而忽略全局信息。此外，我们必须考虑不同蛋白质折叠结构类别的影响。因为不同结构分类的蛋白质表现出不同的生物学功能，而同一结构类别内的蛋白质则具有相似的功能属性。我们提出了LVPocket，一种通过集成Transformer编码器协同捕获蛋白质结构的局部和全局信息的新方法，这有助于模型在结合口袋预测方面获得更好的性能。然后，我们使用迁移学习为四种不同结构类别的蛋白质的数据定制预测模型。这四个微调模型在基线 LVPocket 模型上进行训练，该模型在 sc-PDB 数据集上进行训练。与当前最先进的方法相比，LVPocket 在三个独立数据集上表现出卓越的性能。此外，微调模型在性能方面优于基线模型。我们提出了一种用于预测蛋白质结合袋的新颖模型结构，该结构为依赖大量卷积计算而忽略有关蛋白质结构的全局信息提供了解决方案。此外，我们通过应用迁移学习方法来解决不同蛋白质折叠结构对结合袋预测任务的影响。