Predicting in silico electron ionization mass spectra using quantum chemistry

Journal of Cheminformatics

Dean J. Tantillo¹  Oliver Fiehn²  Tobias Kind²  Shunyang Wang³ 

[1] Department of Chemistry, University of California, 1 Shields Ave, 95616, Davis, CA, USA;West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, 95616, Davis, CA, USA;West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, 95616, Davis, CA, USA;Department of Chemistry, University of California, 1 Shields Ave, 95616, Davis, CA, USA;

关键词: Quantum chemistry;    Similarity score;    Mass spectra;    QCEIMS;   

DOI  :  10.1186/s13321-020-00470-3

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY