Predicting in-silico electron ionization mass spectra using quantum chemistry

Abstract

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.

Simulation of the electron ionization mass spectra of the Novichok nerve agent

“…For each trajectory, an ionization excess energy (IEE) value is assigned using a Poisson distribution, because it is currently prohibitive to calculate it directly 47–49 . We set the maximum IEE probability to 0.6 eV/atom, the value that reportedly yields results closest to reality 48,50 . More details on this distribution function may be found in Grimme and Bauer's work 48…”

“…[47][48][49] We set the maximum IEE probability to 0.6 eV/atom, the value that reportedly yields results closest to reality. 48,50 More details on this distribution function may be found in Grimme and Bauer's work. 48 Subsequently, for each trajectory, the probability that the produced fragments have a positive charge is computed using a method based on the corresponding ionization potentials (IP).…”

Bi-modal Variational Autoencoders for Metabolite Identification Using Tandem Mass Spectrometry

“…Combinatorial in-silico spectra prediction approaches such as CFM-ID 9 , MetFrag 26 , FiD 27 iterate through possible chemical bonds to identify which cleavage events have more probability to result in the observed set of spectral peaks. Other methods are rule-based and rely on pre-defined physical laws, e.g., 28 , Mass Frontier™ (ThermoFisher, CA; HighChem, Bratislava, Slovakia). The choice of a numerical representation of a molecule is a key component of method design.…”