Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12., 7624 Pécs, Hungary.
Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124 Uppsala, Sweden.
Int J Mol Sci. 2022 Jun 30;23(13):7313. doi: 10.3390/ijms23137313.
The human genome codes only a few thousand druggable proteins, mainly receptors and enzymes. While this pool of available drug targets is limited, there is an untapped potential for discovering new drug-binding mechanisms and modes. For example, enzymes with long binding cavities offer numerous prerequisite binding sites that may be visited by an inhibitor during migration from a bulk solution to the destination site. Drug design can use these prerequisite sites as new structural targets. However, identifying these ephemeral sites is challenging. Here, we introduce a new method called NetBinder for the systematic identification and classification of prerequisite binding sites at atomic resolution. NetBinder is based on atomistic simulations of the full inhibitor binding process and provides a networking framework on which to select the most important binding modes and uncover the entire binding mechanism, including previously undiscovered events. NetBinder was validated by a study of the binding mechanism of blebbistatin (a potent inhibitor) to myosin 2 (a promising target for cancer chemotherapy). Myosin 2 is a good test enzyme because, like other potential targets, it has a long internal binding cavity that provides blebbistatin with numerous potential prerequisite binding sites. The mechanism proposed by NetBinder of myosin 2 structural changes during blebbistatin binding shows excellent agreement with experimentally determined binding sites and structural changes. While NetBinder was tested on myosin 2, it may easily be adopted to other proteins with long internal cavities, such as G-protein-coupled receptors or ion channels, the most popular current drug targets. NetBinder provides a new paradigm for drug design by a network-based elucidation of binding mechanisms at an atomic resolution.
人类基因组仅编码了几千种可成药的蛋白质，主要是受体和酶。虽然可用的药物靶点数量有限，但发现新的药物结合机制和模式仍具有未开发的潜力。例如，具有长结合腔的酶提供了许多潜在的必需结合位点，抑制剂在从本体溶液迁移到目标位点的过程中可能会访问这些位点。药物设计可以利用这些前提位点作为新的结构靶点。然而，识别这些短暂的位点是具有挑战性的。在这里，我们引入了一种新的方法，称为 NetBinder，用于在原子分辨率下系统地识别和分类必需结合位点。NetBinder 基于对整个抑制剂结合过程的原子模拟，并提供了一个网络框架，用于选择最重要的结合模式并揭示整个结合机制，包括以前未发现的事件。NetBinder 通过对 blebbistatin（一种有效的抑制剂）与肌球蛋白 2（癌症化疗有前途的靶点）结合机制的研究进行了验证。肌球蛋白 2 是一种很好的测试酶，因为与其他潜在的靶点一样，它具有一个长的内部结合腔，为 blebbistatin 提供了许多潜在的必需结合位点。NetBinder 提出的肌球蛋白 2 在 blebbistatin 结合过程中结构变化的机制与实验确定的结合位点和结构变化非常吻合。虽然 NetBinder 是在肌球蛋白 2 上进行测试的，但它可以很容易地应用于具有长内部腔的其他蛋白质，如 G 蛋白偶联受体或离子通道，这是当前最受欢迎的药物靶点。NetBinder 通过基于网络的原子分辨率阐明结合机制为药物设计提供了一种新的范例。
