Cetin Ebru, Abdizadeh Haleh, Atilgan Ali Rana, Atilgan Canan
Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Türkiye.
J Chem Theory Comput. 2025 May 13;21(9):4910-4920. doi: 10.1021/acs.jctc.5c00193. Epub 2025 Apr 23.
Understanding competitive inhibition at the molecular level is essential for unraveling the dynamics of enzyme-inhibitor interactions and predicting the evolutionary outcomes of resistance mutations. In this study, we present a framework linking competitive inhibition to alchemical free energy perturbation (FEP) calculations, focusing on dihydrofolate reductase (DHFR) and its inhibition by trimethoprim (TMP). Using thermodynamic cycles, we relate experimentally measured binding constants ( and ) to free energy differences associated with wild-type and mutant forms of DHFR with a mean error of 0.9 kcal/mol, providing insight into the molecular underpinnings of TMP resistance. Our findings highlight the importance of local conformational dynamics in competitive inhibition. Mutations in DHFR affect substrate and inhibitor binding affinities differently, influencing the fitness landscape under selective pressure from TMP. Our FEP simulations reveal that resistance mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay of these effects showcases significant molecular-level epistasis in certain cases. The ability to separately assess substrate and inhibitor binding provides valuable insights, allowing for a more precise interpretation of mutation effects and epistatic interactions. Furthermore, we identify key challenges in FEP simulations, including convergence issues arising from charge-changing mutations and long-range allosteric effects. By integrating computational and experimental data, we provide an effective approach for predicting the functional impact of resistance mutations and their contributions to evolutionary fitness landscapes. These insights pave the way for constructing robust mutational scanning protocols and designing more effective therapeutic strategies against resistant bacterial strains.
在分子水平上理解竞争性抑制对于揭示酶 - 抑制剂相互作用的动力学以及预测抗性突变的进化结果至关重要。在本研究中，我们提出了一个将竞争性抑制与炼金术自由能微扰（FEP）计算联系起来的框架，重点关注二氢叶酸还原酶（DHFR）及其被甲氧苄啶（TMP）抑制的情况。利用热力学循环，我们将实验测量的结合常数（和）与野生型和突变型DHFR相关的自由能差异联系起来，平均误差为0.9千卡/摩尔，从而深入了解TMP抗性的分子基础。我们的研究结果突出了局部构象动力学在竞争性抑制中的重要性。DHFR中的突变对底物和抑制剂结合亲和力的影响不同，在TMP的选择压力下影响适应度景观。我们的FEP模拟表明，抗性突变通过特定的结构和/或动力学效应稳定抑制剂结合或底物结合状态。在某些情况下，这些效应的相互作用展示了显著的分子水平上位性。能够分别评估底物和抑制剂结合提供了有价值的见解，从而能够更精确地解释突变效应和上位性相互作用。此外，我们确定了FEP模拟中的关键挑战，包括由电荷变化突变和远程变构效应引起的收敛问题。通过整合计算和实验数据，我们提供了一种有效的方法来预测抗性突变的功能影响及其对进化适应度景观的贡献。这些见解为构建强大的突变扫描方案和设计针对耐药菌株的更有效治疗策略铺平了道路。
