Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.

中文翻译：

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PUResNet：使用深度残差神经网络预测蛋白质配体结合位点

预测蛋白质-配体结合位点是了解蛋白质功能特性的基本步骤，在阐明不同的生物学功能方面起着至关重要的作用，是药物发现的关键步骤。蛋白质在与其相互作用的分子（称为配体）结合后表现出其真实性质，该分子仅在蛋白质结构的有利结合位点结合。已经开发了利用蛋白质特征的不同计算方法来识别蛋白质结构中的结合位点，但似乎都没有提供有希望的结果，因此需要进一步研究。在这项研究中，我们提出了一种深度学习模型 PUResNet 和一种基于结构相似性的新型数据清理过程，用于预测蛋白质-配体结合位点。从整个 scPDB（从蛋白质数据库中提取的可药物结合位点的注释数据库）数据库中，选择了 5020 个蛋白质结构来解决这个问题，用于训练 PUResNet。有了这个，我们在使用距离、体积和比例指标评估两个独立的集合时，取得了比现有方法更好、更合理的性能。