Cappel Daniel, Hall Michelle Lynn, Lenselink Eelke B, Beuming Thijs, Qi Jun, Bradner James, Sherman Woody
Schrödinger GmbH , Dynamostraße 13, 68165 Mannheim, Germany.
Schrodinger Inc. , 120 W 45th Street, New York, New York 10036, United States.
J Chem Inf Model. 2016 Dec 27;56(12):2388-2400. doi: 10.1021/acs.jcim.6b00362. Epub 2016 Nov 18.
A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.
计算机辅助药物设计的一项重大挑战和潜在的高价值应用是准确预测蛋白质-配体结合亲和力。使用分子动力学（MD）采样的自由能微扰（FEP）是实现准确结合自由能预测的最合适方法之一，这是由于该方法具有严格的统计框架、对能量学的正确表示以及对系统中重要自由度（包括明确的水分子）的全面处理。采样方法和力场的最新进展，再加上计算资源的大幅增加，使FEP成为推动从命中物到先导物以及先导物优化的可行技术，从而实现更高效的药物化学循环，并有可能探索更大的化学空间。然而，以前的FEP应用主要集中在以目标的高分辨率晶体结构为起点的系统上，而这在药物发现项目中并不总是可用的。因此，将FEP应用于同源模型的能力将极大地扩展FEP在药物发现中的适用范围。在这项工作中，我们将一种名为FEP+的FEP特定实现应用于与四种不同目标结合的同类配体系列：一种激酶（Tyk2）、一种表观遗传溴结构域（BRD4）、一种跨膜GPCR（A）和一个蛋白质-蛋白质相互作用界面（BCL-2家族蛋白MCL-1）。我们以晶体结构和同源模型为起点应用FEP+，发现使用同源模型的性能通常与使用晶体结构时的结果相当。计算对输入模型结构变化的稳健性可能归因于分子动力学模拟中的构象采样，这使得建模的受体能够适应系列中每个配体的“真实”构象。这项工作例证了使用具有全系统灵活性的全原子模拟方法的优势，并为FEP在同源模型中的普遍应用带来了希望，不过还应进行额外的验证研究，以进一步了解该方法的局限性以及FEP效果最佳的场景。
