Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
J Comput Chem. 2025 Jan 5;46(1):e27478. doi: 10.1002/jcc.27478. Epub 2024 Oct 5.
In protein-ligand docking, the score assigned to a protein-ligand complex is approximate. Especially, the internal energy of the ligand is difficult to compute precisely using a molecular mechanics based force-field, introducing significant noise in the rank-ordering of ligands. We propose an open-source protocol (https://github.com/UnixJunkie/MMO), using two quantum mechanics (QM) single point energy calculations, plus a Monte Carlo (Monte Carlo) based ligand minimization procedure in-between, to estimate ligand strain after docking. The MC simulation uses the ANI-2x (QM approximating) force field and is performed in the dihedral space. On some protein targets, using strain filtering after docking allows to significantly improve hit rates. We performed a structure-based virtual screening campaign on nine protein targets from the Laboratoire d'Innovation Thérapeutique-PubChem assays dataset using Cambridge crystallographic data centre genetic optimization for ligand docking. Then, docked ligands were submitted to the strain estimation protocol and the impact on hit rate was analyzed. As for docking, the method does not always work. However, if sufficient active and inactive molecules are known for a given protein target, its efficiency can be evaluated.
在蛋白质配体对接中,分配给蛋白质-配体复合物的分数是近似的。特别是,使用基于分子力学的力场精确计算配体的内能非常困难,这会导致配体排序中存在显著的噪声。我们提出了一个开源协议(https://github.com/UnixJunkie/MMO),使用两次量子力学(QM)单点能计算,以及两次之间的基于蒙特卡罗(Monte Carlo)的配体最小化过程,来估计对接后配体的应变。MC 模拟使用 ANI-2x(QM 近似)力场,并在二面角空间中进行。在某些蛋白质靶标上,对接后使用应变过滤可以显著提高命中率。我们使用剑桥晶体学数据中心遗传优化对来自 Laboratoire d'Innovation Thérapeutique-PubChem 测定数据集的九个蛋白质靶标进行了基于结构的虚拟筛选,然后将对接的配体提交给应变估计协议,并分析了其对命中率的影响。与对接一样,该方法并非总是有效。但是,如果对于给定的蛋白质靶标已知足够多的活性和非活性分子,则可以评估其效率。