Wang Shunyang, Kind Tobias, Tantillo Dean J, Fiehn Oliver
West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, CA, 95616, USA.
Department of Chemistry, University of California, 1 Shields Ave, Davis, CA, 95616, USA.
J Cheminform. 2020 Oct 20;12(1):63. doi: 10.1186/s13321-020-00470-3.
Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.
通过质谱进行化合物鉴定需要参考质谱图。虽然PubChem中有超过1.02亿种化合物，但美国国家标准与技术研究院（NIST）或代谢组学网络分析（MoNA）质谱数据库中可获得的经过整理的电子电离（EI）质谱图不到30万张。在此，我们测试量子化学方法（QCEIMS），通过将分子动力学（MD）与统计方法相结合来生成计算机模拟EI质谱（MS）。为了测试预测的准确性，我们生成了451个小分子的计算机模拟质谱图，并与NIST 17质谱库中的实验光谱进行了比较。这些化合物涵盖43个化学类别，分子量高达358道尔顿。有机氧化合物的匹配准确率较低，而计算时间随分子大小呈指数增加。我们探索了参数空间以提高预测准确性，包括初始温度、MD轨迹数量和碰撞过剩能量（IEE）。构象灵活性与预测准确性无关。总体而言，QCEIMS可以从第一原理预测化学物质的70 eV电子电离光谱。在能够大规模生成新分子的QCEIMS质谱图之前，仍需要改进计算势能面（PES）的方法。
