Realistic sampling of amino acid geometries for a multipolar polarizable force field.
Realistic sampling of amino acid geometries for a multipolar polarizable force field.
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DOI：
10.1002/jcc.24006
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发表时间：
2015-09-15
影响因子：
3
通讯作者：
Popelier PL
中科院分区：
文献类型：
--
作者：
Hughes TJ;Cardamone S;Popelier PL
量子化学拓扑力场（QCTFF）使用机器学习方法克里金法将原子多极矩映射到分子系统中所有原子的坐标。重要的是，克里金法在相关且现实的分子几何训练集上进行操作。因此，我们直接从蛋白质数据库（PDB）中存储的蛋白质晶体结构中采样单个氨基酸几何结构。这种采样增强了训练几何形状的构象真实性（就二面角而言）。然而，由于 X 射线衍射图案的细化过程的人为因素以及实验上不可见的氢原子，这些几何形状可能充满不准确的键长和价角。这就是为什么我们开发了一种称为 PDB/NM 的混合 PDB/非平稳正态模式 (NM) 采样方法。该方法优于标准 NM 采样，后者仅捕获从气相中单个氨基酸的固定点优化的几何结构。事实上，PDB/NM 将相关二面角的采样与化学上正确的局部几何形状结合起来。使用 PDB/NM 采样的几何形状用于构建丙氨酸和赖氨酸的克里格模型，并将其预测准确性与从其他三种采样方法采样的几何形状构建的模型进行比较。与二面角的变化相反，键长的变化会给预测准确性带来压力，并可能降低预测准确性。因此，与迄今为止在 QCTFF 开发中使用的围绕局部能量最小值的 NM 采样相比，PDB/NM 方法更大的二面角覆盖范围不会降低克里金模型的预测精度。 © 2015 作者。计算化学杂志由 Wiley periodicals, Inc. 出版
The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB). This sampling enhances the conformational realism (in terms of dihedral angles) of the training geometries. However, these geometries can be fraught with inaccurate bond lengths and valence angles due to artefacts of the refinement process of the X‐ray diffraction patterns, combined with experimentally invisible hydrogen atoms. This is why we developed a hybrid PDB/nonstationary normal modes (NM) sampling approach called PDB/NM. This method is superior over standard NM sampling, which captures only geometries optimized from the stationary points of single amino acids in the gas phase. Indeed, PDB/NM combines the sampling of relevant dihedral angles with chemically correct local geometries. Geometries sampled using PDB/NM were used to build kriging models for alanine and lysine, and their prediction accuracy was compared to models built from geometries sampled from three other sampling approaches. Bond length variation, as opposed to variation in dihedral angles, puts pressure on prediction accuracy, potentially lowering it. Hence, the larger coverage of dihedral angles of the PDB/NM method does not deteriorate the predictive accuracy of kriging models, compared to the NM sampling around local energetic minima used so far in the development of QCTFF. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.