Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.
Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.
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DOI：
10.1021/acs.jcim.3c01350
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发表时间：
2023-11-13
影响因子：
5.6
通讯作者：
Wang, Junmei
中科院分区：
文献类型：
--
作者：
Han, Fengyang;Hao, Dongxiao;He, Xibing;Wang, Luxuan;Niu, Taoyu;Wang, Junmei
在这项研究中，我们使用 ANI-2X（一种机器学习潜力），结合我们最近开发的一种几何优化算法（称为回溯线搜索共轭梯度 (CG-BS)），系统地研究了小分子配体在其构象集合中的生物活性构象的能量分布。我们首先使用两个分子集评估了这些方法的组合（ANI-2X/CG-BS）。对于231分子组，在ωB97X/6-31G(d)和B3LYP-D3BJ/DZVP水平上进行从头计算以进行精度比较，而对于8,992分子组，在B3LYP-D3BJ/DZVP水平上进行从头计算。对于两个分子集中的每个分子，最多生成 10 个构象，这减少了单个异常值对性能评估的影响。受到 ANI-2x/CG-BS 在这些评估中的表现的鼓舞，我们使用 ANI-2x/CG-BS 计算了蛋白质数据库 (PDB) 中超过 27,000 个配体的能量分布。每个配体具有至少一种与生物分子结合的构象，并且该配体构象被标记为结合构象。除了结合构象外，每个构象还使用 OpenEye 的 Omega2 软件 (omega/) 生成多达 200 个构象。我们对 17,197 个 PDB 配体的结合构象能在系综中的分布进行了统计分析，这些配体的结合构象能在 Omega2 生成的构象系综的能量范围内。我们发现，与整体构象相比，一半配体的结合构象的相对构象能低于2.91 kcal/mol，并且约90%的结合构象比整体构象能高出10 kcal/mol以内。该信息可用于指导基于形状的虚拟筛选的库的构建，并改进对接算法以有效地对结合构象进行采样。
In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye’s Omega2 software ( omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.
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DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
DOI：
10.3390/molecules27238567
发表时间：
2022-12-05
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Wang Y;Walker BD;Liu C;Ren P
通讯作者：
Ren P
影响因子：
15
作者：
Wang, Lingle;Wu, Yujie;Abel, Robert
通讯作者：
Abel, Robert
影响因子：
8.6
作者：
Behler, Joerg;Parrinello, Michele
通讯作者：
Parrinello, Michele
影响因子：
5.6
作者：
Sitzmann M;Weidlich IE;Filippov IV;Liao C;Peach ML;Ihlenfeldt WD;Karki RG;Borodina YV;Cachau RE;Nicklaus MC
通讯作者：
Nicklaus MC