An ANI-2 enabled open-source protocol to estimate ligand strain after docking

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2024-10-05 DOI:10.1002/jcc.27478
Francois Berenger, Koji Tsuda
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Abstract

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.

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启用 ANI-2 的开源协议,可在对接后估算配体应变
在蛋白质配体对接中,蛋白质配体复合物的得分是近似的。特别是配体的内能很难通过基于分子力学的力场精确计算,从而给配体的排序带来很大的噪声。我们提出了一种开源协议 (https://github.com/UnixJunkie/MMO),利用两次量子力学(QM)单点能量计算,加上中间基于蒙特卡罗(Monte Carlo)的配体最小化程序,来估计对接后的配体应变。蒙特卡洛模拟使用 ANI-2x(QM 近似)力场,并在二面空间中进行。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。我们利用剑桥晶体学数据中心的基因优化配体对接,对来自 Laboratoire d'Innovation Thérapeutique-PubChem 检测数据集的九种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,并分析其对命中率的影响。至于对接,该方法并不总是有效。不过,如果已知特定蛋白质靶点有足够的活性和非活性分子,就可以评估其效率。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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