A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein–ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein–ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein–ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org .

中文翻译：

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PSnpBind：使用多线程虚拟筛选工作流程构建的突变结合位点蛋白质-配体复合物数据库


药物设计的一个关键概念是自然变异，尤其是发生在药物靶点结合位点的自然变异，如何通过改变结合亲和力影响个体间的药物反应和功效。这些效应已在非常有限的小型数据集上进行了研究，而理想情况下，需要由于结合位点单核苷酸多态性 (SNP) 而导致的结合亲和力变化的大型数据集进行评估。然而，据我们所知，这样的数据集并不存在。因此，使用分子对接方法构建了配体与蛋白质及其所有报道的结合位点变体的结合亲和力的参考数据集。拥有涵盖广泛的结合袋突变和大的小分子景观的蛋白质-配体复合物的大型数据库对于多种类型的研究非常重要。例如，开发机器学习算法来预测蛋白质-配体亲和力或 SNP 对其的影响需要大量数据。在这项工作中，我们展示了 PSnpBind：使用多线程虚拟筛选工作流程构建的包含 60 万个突变结合位点蛋白质-配体复合物的大型数据库。它提供了一个网络界面来探索和可视化蛋白质-配体复合物，并提供一个 REST API 来以编程方式访问数据库内容的不同方面。 PSnpBind 是开源的，可在 https://psnpbind.org 上免费获取。