Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.

中文翻译：

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3D U-Net：一种基于体素的蛋白质结构结合位点预测方法

新蛋白质的结合位点预测在基于结构的药物设计中很重要。当没有关于其口袋的信息可用时，确定的结合位点可能有助于开发针对世界上新病毒爆发的治疗方法，而 COVID-19 就是一个很好的例子。作为一种替代方法，使用计算方法识别口袋最近引起了很多兴趣。在这项研究中，结合位点预测被视为一个语义分割问题。使用基于骰子损失函数的改进的 3D 版本的 U-Net 模型来准确预测结合位点。所提出的模型在独立测试数据集和 SARS-COV-2 上的性能表明，与最近发布的深度学习模型相比，分割模型可以以更准确的形状预测结合位点，即。e. 深站点。因此，该模型可能有助于预测蛋白质的结合位点，并可用于新型蛋白质的药物设计。