Computational Medicine Program and Department of Pharmacology, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States.
J Chem Theory Comput. 2024 Jul 23;20(14):5829-5841. doi: 10.1021/acs.jctc.4c00502. Epub 2024 Jul 13.
Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.
结合热力学和动力学在药物设计中起着关键作用。然而,由于模拟时间尺度有限,使用传统的分子动力学(cMD)高效预测小分子和灵活肽的配体结合热力学和动力学一直具有挑战性。基于我们之前开发的配体高斯加速分子动力学(LiGaMD)方法,我们提出了一种新方法,称为“LiGaMD3”,其中我们在三个对小分子/肽解离、再结合和系统构象变化起重要作用的单独能量项中引入三重提升,以提高小分子/肽与靶蛋白相互作用的采样效率。为了验证 LiGaMD3 的性能,选择 MDM2 与小分子(Nutlin 3)和两个高度灵活的肽(PMI 和 P53)结合作为模型系统。LiGaMD3 可以在 2 μs 的模拟中有效地捕获重复的小分子/肽解离和结合事件。LiGaMD3 预测的结合动力学常数速率和自由能与可用的实验值和先前的模拟结果一致。因此,LiGaMD3 提供了一种更通用和高效的方法来捕获小分子配体和灵活肽的解离和结合,从而能够准确预测它们的结合热力学和动力学。