Schrodinger Inc., 1540 Broadway, New York, New York 10036, United States.
J Chem Theory Comput. 2023 Jun 13;19(11):3080-3090. doi: 10.1021/acs.jctc.3c00009. Epub 2023 May 23.
Structure-based drug design frequently operates under the assumption that a single holo structure is relevant. However, a large number of crystallographic examples clearly show that multiple conformations are possible. In those cases, the protein reorganization free energy must be known to accurately predict binding free energies for ligands. Only then can the energetic preference among these multiple protein conformations be utilized to design ligands with stronger binding potency and selectivity. Here, we present a computational method to quantify these protein reorganization free energies. We test it on two retrospective drug design cases, Abl kinase and HSP90, and illustrate how alternative holo conformations can be derisked and lead to large boosts in affinity. This method will allow computer-aided drug design to better support complex protein targets.
基于结构的药物设计通常假设单一的全酶结构是相关的。然而，大量的晶体学实例清楚地表明，存在多种构象是可能的。在这些情况下，必须知道蛋白质重组自由能，以准确预测配体的结合自由能。只有这样，才能利用这些多种蛋白质构象之间的能量偏好来设计具有更强结合效力和选择性的配体。在这里，我们提出了一种计算方法来量化这些蛋白质重组自由能。我们在两个回顾性药物设计案例（Abl 激酶和 HSP90）中进行了测试，并说明了如何降低替代全酶构象的风险，并导致亲和力的大幅提高。这种方法将使计算机辅助药物设计能够更好地支持复杂的蛋白质靶标。
