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Quantum Chemical Prediction of Electron Ionization Mass Spectra of Trimethylsilylated Metabolites
... While over 116 million known compounds have been recorded in PubChem (August, 2023), only 347,000 unique compounds have EI mass spectra in the NIST library [6]. The in-silico generation of reference spectra, including quantum chemistry molecular dynamics simulation [9,10], still has difficulties in prediction accuracy. Compound annotation based on calculating fragmentation trees and fingerprint prediction for mass spectra [11] is an alternative strategy that does not require a reference spectral 2 of 9 library. ...
... Exceptions were found for [M + C 2 H 5 ] + and [M + C 3 H 5 ] + , which were mostly found at <5% bp intensity. Occasionally, additional ions were observed at lower intensity, as described before [10,11]. A Python script based on those fragmentation patterns was developed to identify CI patterns by finding these isotopic ion groups and utilizing the nominal mass difference between them ( Figure 1). ...
... Metabolites 2023, 13, x FOR PEER REVIEW 4 of 10 were found for [M + C2H5] + and [M + C3H5] + , which were mostly found at <5% bp intensity. Occasionally, additional ions were observed at lower intensity, as described before [10,11]. A Python script based on those fragmentation patterns was developed to identify CI patterns by finding these isotopic ion groups and utilizing the nominal mass difference between them (Figure 1). ...
Gas chromatography–mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M − CH3]+ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]+ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks.
During the past decade promising methods for computational prediction of electron ionization mass spectra have been developed. The most prominent ones are based on quantum chemistry (QCEIMS) and machine learning (CFM-EI, NEIMS). Here we provide a threefold comparison of these methods with respect to spectral prediction and compound identification. We found that there is no unambiguous way to determine the best of these three methods. Among other factors, we find that the choice of spectral distance functions play an important role regarding the performance for compound identification.
The high-throughput ability and sensitivity of mass spectrometry make it the most popular
platform for a metabolomics study. Thus, identifying small molecules from mass spectra plays a
central role in metabolomics. There are many computational techniques for mass spectra raw data
processing, including feature detection, peak alignment, and mass spectral deconvolution. This
dissertation focuses on the conversion between spectral and structural information. It is a
spontaneous thought to identify compound structure from mass spectra by searching the query
spectra against a reference library with similarity score. However, this approach is limited by the
availability of reference spectra and standard compounds. To bridge the gap, different computation
tools based on fragmentation trees are developed to help annotate spectra. The other thought is to
generate in-silico spectra from the structural information. Several compound databases, such as
PubChem, KEGG, HMDB and CHEBI can be the source of structural information. Machine
learning and heuristic approaches are trained from spectral knowledge to generate in-silico spectra.
However, all those approaches cannot predict beyond the known data. As a first-principles method,
Quantum chemistry modeling is only based on the rules of quantum mechanics and can help us
explore the unknown space of metabolites. Yet, the quantum chemical simulation on molecules
over 600 Da is too expensive to get accurate results. Chemical ionization with methane reagent
gas can help identify the molecular ion species and help the unknown metabolite identification.
This dissertation describes computational studies using molecular dynamics on in-silico mass
spectra of small molecule generation. Chapter 1 provides an overview of applications of quantum
chemistry to generate in-silico mass spectra. Chapter 2 showcases the performance of quantum
chemistry simulations on small molecules and probes the parameter space to find potential
improvements to the existing method. The conformational flexibility effect is also explored and
iv
has no correlation with prediction accuracy. Chapter 3 describes a new workflow to include
chemical derivation, especially silylation in the quantum chemistry calculation. Different
compound classes including organic acid, alcohol, amide, amine and thiols are simulated and
compared against experimental mass spectra. The molecular dynamics trajectories are also
investigated to find missed fragmentations from rearrangements. Chapter 4 provides a new
algorithm that introduces excited state calculation into the molecular dynamics prediction of mass
spectra. The new algorithm can predict more fragmentation reactions that are missing in previous
studies and as a result, the mass spectral similarity scores are increased, and simulation is more
accurate. Chapter 4 also discusses the limitations of molecular dynamics simulation time and the
lack of rearrangement reactions. Chapter 5 provides another routine from the experimental side to
help unknown metabolites identification with methane chemical ionization and quadrupole-time
of flight mass spectrometry.
Analysis and validation of a mass spectrometry (MS) experiment are usually performed by comparison to reference spectra. However, if references are missing, measured spectra cannot be properly matched. To close this gap, the Quantum Chemical Mass Spectrometry (QCxMS) program has been developed. It enables fully automatic calculations of electron ionization (EI) and positive ion collision-induced dissociation (CID) mass spectra of singly charged molecular ions. In this work, the extension to negative and multiple ion charge for the CID run mode is presented. QCxMS is now capable of calculating structures carrying any charge, without the need for pretabulated fragmentation pathways or machine learning of database spectra. Mass spectra of four single negatively charged and two multiple positively charged organic ions with molecular sizes from 12 to 92 atoms were computed and compared to reference spectra. The underlying Born-Oppenheimer molecular dynamics (MD) calculations were conducted using the semiempirical quantum mechanical GFN2-xTB method, while for some small molecules, ab initio DFT-based MD simulations were performed. Detailed insights into the fragmentation pathways were gained, and the effects of the computed charge assignments on the resulting spectrum are discussed. Especially for the negative ion mode, the influence of the deprotonation site to create the anion was found to be substantial. Doubly charged fragments could successfully be calculated fully automatically for the first time, while higher charged structures introduced severe assignment problems. Overall, this extension of the QCxMS program further enhances its applicability and underlines its value as a sophisticated toolkit for CID-based tandem MS structure elucidation.
A computational method for the untargeted determination of cycling yeast metabolites using a comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) dataset is presented. The yeast metabolomic cycle for the diploid yeast strain CEN. PK with a 5 h cycle period relative to the O2 concentration level is comprehensively examined to determine the metabolites that exhibit cycling. Samples were collected over only two cycles (10 h with a total of 24 time-point sampling intervals at 25 min each) as an experimental constraint. Due to the limited number of cycles expressed in the dataset, a computational method was devised to determine with statistical significance whether or not a given metabolite exhibited a temporal signal pattern that constituted cycling in the context of the 5 h cycle period. The computational method we report compares the experimentally obtained 24 time-point metabolite signal sequences to randomly generated signal sequences coupled with statistically based confidence level LOF metrics to determine whether or not a given metabolite expresses cycling, and if so, what is the phase of the cycling. Initially the GC × GC-TOFMS dataset was analyzed using tile-based Fisher ratio (F-ratio) analysis. Since there were 24 time-point intervals, this constituted 24 sample classes in the F-ratio calculation which produced 672 metabolite hits. Next, application of the computational method determined that there were 210 of the 672 metabolites exhibiting cycling: 55 identified metabolites and 155 unknown metabolites. Furthermore, the 210 cycling metabolites were categorized into four groups, and where applicable, a phase determined: 1 cycle/5 h period (106 metabolites), 2 cycles/5 h period (13 metabolites), spiky pattern (12 metabolites), or multimodal pattern (79 metabolites).
A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.
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.
Abstract Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.
This review describes the mass spectral fragmentation of trimethylsilyl (TMS) and related alkylsilyl derivatives used for preparing samples for analysis, mainly by combined gas chromatography and mass spectrometry (GC/MS). The review is divided into three sections. The first section is concerned with the TMS derivatives themselves and describes fragmentation of derivatized alcohols, thiols, amines, ketones, carboxylic acids and bifunctional compounds such as hydroxy- and amino-acids, halo acids and hydroxy ethers. More complex compounds such as glycerides, sphingolipids, carbohydrates, organic phosphates, phosphonates, steroids, vitamin D, cannabinoids, and prostaglandins are discussed next. The second section describes intermolecular reactions of siliconium ions such as the TMS cation and the third section discusses other alkylsilyl derivatives. Among these latter compounds are di- and trialkyl-silyl derivatives, various substituted-alkyldimethylsilyl derivatives such as the tert-butyldimethylsilyl ethers, cyclic silyl derivatives, alkoxysilyl derivatives, and 3-pyridylmethyldimethylsilyl esters used for double bond location in fatty acid spectra. © 2019 Wiley Periodicals, Inc. Mass Spec Rev 0000:1-107, 2019.
In this work, we have tested two different extended tight-binding methods in the framework of the quantum chemistry electron ionization mass spectrometry (QCEIMS) program to calculate electron ionization mass spectra. The QCEIMS approach provides reasonable, first-principles computed spectra, which can be directly compared to experiment. Furthermore, it provides detailed insight into the reaction mechanisms of mass spectrometry experiments. It sheds light upon the complicated fragmentation procedures of bond breakage and structural rearrangements that are difficult to derive otherwise. The required accuracy and computational demands for successful reproduction of a mass spectrum in relation to the underlying quantum chemical method are discussed. To validate the new GFN2-xTB approach, we conduct simulations for 15 organic, transition-metal, and main-group inorganic systems. Major fragmentation patterns are analyzed, and the entire calculated spectra are directly compared to experimental data taken from the literature. We discuss the computational costs and the robustness (outliers) of several calculation protocols presented. Overall, the new, theoretically more sophisticated semiempirical method GFN2-xTB performs well and robustly for a wide range of organic, inorganic, and organometallic systems.
When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.
Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography–mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography–mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives.
Mass spectrometry-based untargeted metabolomics often detects statistically significant metabolites that cannot be readily identified. Without defined chemical structure, interpretation of the biochemical relevance is not feasible. Epimetabolites are produced from canonical metabolites by defined enzymatic reactions and may represent a large fraction of the structurally unidentified metabolome. We here present a systematic workflow for annotating unknown epimetabolites using high resolution gas chromatography - accurate mass spectrometry with multiple ionization techniques and stable isotope labeled derivatization methods. We first determine elemental formulas, which are then used to query the 'metabolic in-silico expansion' database (MINE DB) to obtain possible molecular structures that are predicted by enzyme promiscuity from canonical pathways. Accurate mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER to derive the best candidates. We validated the workflow by correctly identifying ten methylated nucleosides and six methylated amino acids. We then employed this strategy to annotate eight unknown compounds from cancer studies and other biological systems.
Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.
Background
Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible.
Results
We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper.
Conclusion
ClassyFire, in combination with ChemOnt (ClassyFire’s comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million “ClassyFire” classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks.
We report a new approach to investigating the mechanisms of fast peptide cation-radical dissociations based on an analysis of time-resolved reaction progress by Ehrenfest dynamics, as applied to an Ala-Arg cation-radical model system. Calculations of stationary points on the ground electronic state that were carried out with effective CCSD(T)/6-311++G(3df,2p) could not explain the experimental branching ratios for loss of a hydrogen atom, ammonia, and N-C(α) bond dissociation in (AR + 2H)(+•). The Ehrenfest dynamics results indicate that the ground and low-lying excited electronic states of (AR + 2H)(+•) follow different reaction courses in the first 330 femtoseconds after electron attachment. The ground (X) state undergoes competing loss of N-terminal ammonia and isomerization to an aminoketyl radical intermediate that depend on the vibrational energy of the charge-reduced ion. The A and B excited states involve electron capture in the Arg guanidine and carboxyl groups and are non-reactive on the short time scale. The C state is dissociative and progresses to a fast loss of an H atom from the Arg guanidine group. Analogous results were obtained by using the B3LYP and CAM-B3LYP density functionals for the excited state dynamics and including the universal M06-2X functional for ground electronic state calculations. The results of this Ehrenfest dynamics study indicate that reaction pathway branching into the various dissociation channels occurs in the early stages of electron attachment and is primarily determined by the electronic states being accessed. This represents a new paradigm for the discussion of peptide dissociations in electron based methods of mass spectrometry.
ABSTRACT:
A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats.
We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion.
Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
At least two independent parameters are necessary for compound identification in metabolomics. We have compiled 2 212 electron impact mass spectra and retention indices for quadrupole and time-of-flight gas chromatography/mass spectrometry (GC/MS) for over 1000 primary metabolites below 550 Da, covering lipids, amino acids, fatty acids, amines, alcohols, sugars, amino-sugars, sugar alcohols, sugar acids, organic phosphates, hydroxyl acids, aromatics, purines, and sterols as methoximated and trimethylsilylated mass spectra under electron impact ionization. Compounds were selected from different metabolic pathway databases. The structural diversity of the libraries was found to be highly overlapping with metabolites represented in the BioMeta/KEGG pathway database using chemical fingerprints and calculations using Instant-JChem. In total, the FiehnLib libraries comprised 68% more compounds and twice as many spectra with higher spectral diversity than the public Golm Metabolite Database. A range of unique compounds are present in the FiehnLib libraries that are not comprised in the 4345 trimethylsilylated spectra of the commercial NIST05 mass spectral database. The libraries can be used in conjunction with GC/MS software but also support compound identification in the public BinBase metabolomic database that currently comprises 5598 unique mass spectra generated from 19,032 samples covering 279 studies of 47 species (plants, animals, and microorganisms).
Unlabelled:
Metabolomics, in particular gas chromatography-mass spectrometry (GC-MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here we present GMD, The Golm Metabolome Database, an open access metabolome database, which should enable these processes. GMD provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds. The main goal will be the representation of an exchange platform for experimental research activities and bioinformatics to develop and improve metabolomics by multidisciplinary cooperation.
Availability:
http://csbdb.mpimp-golm.mpg.de/gmd.html
Contact:
Steinhauser@mpimp-golm.mpg.de
Supplementary information:
http://csbdb.mpimp-golm.mpg.de/
A single trajectory (ST) direct dynamics approach is compared with quasiclassical trajectory (QCT) direct dynamics calculations for determining product energy partitioning in unimolecular dissociation. Three comparisons are made by simulating C2 H5 F→HF+ C2 H4 product energy partitioning for the MP26-31 G* and MP26-311++ G** potential energy surfaces (PESs) and using the MP26-31 G* PES for C2 H5 F dissociation as a model to simulate CHCl2 CCl3 →HCl+ C2 Cl4 dissociation and its product energy partitioning. The trajectories are initiated at the transition state with fixed energy in reaction-coordinate translation Et‡. The QCT simulations have zero-point energy (ZPE) in the vibrational modes orthogonal to the reaction coordinate, while there is no ZPE for the STs. A semiquantitative agreement is obtained between the ST and QCT average percent product energy partitionings. The ST approach is used to study mass effects for product energy partitioning in HX(X=F or Cl) elimination from halogenated alkanes by using the MP26-31 G* PES for C2 H5 F dissociation and varying the masses of the C, H, and F atoms. There is, at most, only a small mass effect for partitioning of energy to HX vibration and rotation. In contrast, there are substantial mass effects for partitioning to relative translation and the polyatomic product's vibration and rotation. If the center of mass of the polyatomic product is located away from the C atom from which HX recoils, the polyatomic has substantial rotation energy. Polyatomic products, with heavy atoms such as Cl atoms replacing the H atoms, receive substantial vibration energy that is primarily transferred to the wag-bend motions. For Et‡ of 1.0 kcalmol, the ST calculations give average percent partitionings to relative translation, polyatomic vibration, polyatomic rotation, HX vibration, and HX rotation of 74.9%, 6.8%, 1.5%, 14.4%, and 2.4% for C2 H5 F dissociation and 39.7%, 38.1%, 0.2%, 16.1%, and 5.9% for a model of CHCl2 CCl3 dissociation.
Mass spectrometry (MS) is a powerful tool in chemical research and substance identification. For the computational modeling of electron ionization MS, we have developed the quantum-chemical electron ionization mass spectra (QCEIMS) program. Here, we present an extension of QCEIMS to calculate collision-induced dissociation (CID) spectra. The more general applicability is accounted for by the new name QCxMS, where "x" refers to EI or CID. To this end, fragmentation and rearrangement reactions are computed "on-the-fly" in Born-Oppenheimer molecular dynamics (MD) simulations with the semiempirical GFN2-xTB Hamiltonian, which provides an efficient quantum mechanical description of all elements up to Z = 86 (Rn). Through the explicit modeling of multicollision processes between precursor ions and neutral gas atoms as well as temperature-induced decomposition reactions, QCxMS provides detailed insight into the collision kinetics and fragmentation pathways. In combination with the CREST program to determine the preferential protonation sites, QCxMS becomes the first standalone MD-based program that can predict mass spectra based solely on molecular structures as input. We demonstrate this for six organic molecules with masses ranging from 159 to 296 Da, for which QCxMS yields CID spectra in reasonable agreement with experiments.
Organic pollutants can be identified by comparing their electron ionization (EI) mass spectra with those in libraries or obtained from authentic standards. Nevertheless, libraries are incomplete; standards may be unavailable or too costly, or their synthesis may be too time-consuming. This study evaluates the performance of quantum chemical electron ionization mass spectrometry (QCEIMS) vis-à-vis competitive fragmentation modeling (CFM) for suspect screening and unknown identification. EI mass spectra of 35 compounds, including halogenated organics, organophosphorus flame retardants (OPFRs), and disinfection byproducts were computed. Computational results were compared with EI mass spectra compiled in the NIST Library as well as collision-induced dissociation (CID) mass spectra obtained from radical cations M•+ generated by charge-exchange atmospheric pressure chemical ionization (APCI). The results indicate that QCEIMS performs equivalently or better than CFM. Average match factors between computed and experimental (NIST) EI mass spectra were 656 vs 503 for the halogenated organics, and on average, QCEIMS predicted 55% of the products generated by CID vs 17% predicted by CFM. QCEIMS predicted 37% of the OPFR CID products whereas CFM predicted 29%. QCEIMS performed comparably to a commercial combinatorial fragmentation method for suspect screening of a dust sample, identifying 19/20 targets. Examples of unknown pollutants, whose reference spectra were unavailable at the time of discovery, are also presented. The computational results suggest that QCEIMS can help guide the analyst in obtaining authentic standards and raise the possibility that, with advances in computing, an unknown may eventually be confirmed in hours as opposed to the days or months required to obtain authentic standards.
An extended semiempirical tight-binding model is presented, which is primarily designed for the fast calculation of structures and noncovalent interaction energies for molecular systems with roughly 1000 atoms. The essential novelty in this so-called GFN2-xTB method is the inclusion of anisotropic second order density fluctuation effects via short-range damped interactions of cumulative atomic multipole moments. Without noticeable increase in the computational demands, this results in a less empirical and overall more physically sound method, which does not require any classical halogen or hydrogen bonding corrections and which relies solely on global and element-specific parameters (available up to radon, Z = 86). Moreover, the atomic partial charge dependent D4 London dispersion model is incorporated self-consistently, which can be naturally obtained in a tight-binding picture from second order density fluctuations. Fully analytical and numerically precise gradients (nuclear forces) are implemented. The accuracy of the method is benchmarked for a wide variety of systems and compared with other semiempirical methods. Along with excellent performance for the "target" properties, we also find lower errors for "off-target" properties such as barrier heights and molecular dipole moments. High computational efficiency along with the improved physics compared to its precursor GFN-xTB makes this method well-suited to explore the conformational space of molecular systems. Significant improvements are furthermore observed for various benchmark sets, which are prototypical for biomolecular systems in aqueous solution.
No books have been completely devoted to all aspects of derivatisation for various mass spectrometric investigations, especially for the comparatively new soft-ionisation techniques. Considerable experience in mass spectrometric practice and in the development and application of preliminary chemical modification of organic compounds impelled the authors to write A Handbook of Derivatives for Mass Spectrometry (513 pages). It is based upon the eight reviews published in European Journal of Mass Spectrometry with the addition of a chapter devoted to derivatisation for the soft-ionisation MS of large molecules.
Individual chapters of A Handbook of Derivatives for Mass Spectrometry can be purchased as PDF downloads
Electron ionisation mass spectra of N-methylimidazolidin-4-one organocatalysts were studied by experimental and theoretical means. The molecular ions mostly undergo alpha cleavages of exocyclic substituents that leave the five-membered ring intact. The type of substituent strongly dominates the appearance of the spectra. Fragmentation cascades are corroborated by metastable ion mass spectra. Quantum Chemistry Electron Ionization Mass Spectra calculations correlate reasonably well with the experimental electron ionization spectra and reveal mechanistic details of fragmentation pathways. The drawbacks and benefits of such calculations are discussed.
We propose a novel, special purpose semi-empirical tight binding (TB) method for the calculation of structures, vibrational frequencies, and non-covalent interactions of large molecular systems with 1000 or more atoms. The functional form of the method is related to the self-consistent density functional TB scheme and mostly avoids element-pair specific parameters. The parameterization covers all spd-block elements and the lanthanides up to Z=86 using reference data at the hybrid density functional theory level. Key features of the Hamiltonian are the use of partially polarized Gaussian type orbitals, a double-zeta orbital basis for hydrogen, atomic-shell charges, diagonal third-order charge fluctuations, coordination number-dependent energy levels, a non-covalent halogen-bond potential, and the well established D3 dispersion correction. The accuracy of the method, called GFN-xTB (Geometry, Frequency, Non-covalent, eXtended TB), is extensively benchmarked for various systems in comparison with existing semi-empirical approaches, and the method is applied to a few representative structural problems in chemistry.
The comparison of experimental and predicted kinetic isotope effects in the α-cleavage of alkoxy radicals is used here to judge the applicability of statistical rate theories. It is found that the governing rate theory and the statistical versus nonstatistical nature of the cleavage depend on the cleavage barrier and how much energy is imparted to the radical. The latter can then be controlled by changing the size of substituents in the system. With a large alkyl group substituent, the vibrational energy of the alkoxy radical is increased but this energy is not statistically distributed, lead-ing to a lower isotope effect than predicted by statistical the-ories. The observed isotope effect can be approximately ra-tionalized using a semi-statistical localized RRKM model.
Mass spectrometry-based untargeted metabolomics detects many peaks that cannot be identified. While advances have been made for automatic structure annotations in LC-electrospray-MS/MS, no open source solutions are available for hard electron ionization used in GC-MS. In metabolomics, most compounds bear moieties with acidic protons, for example, amino, hydroxyl, or carboxyl groups. Such functional groups increase the boiling points of metabolites too much for use in GC-MS. Hence, in GC-MS-focused metabolomics, derivatization of these groups is essential and has been employed since the 1960s. Specifically, trimethylsilylation is known as mild and universal method for GC-MS analysis. Here, we comprehensively compile accurate mass fragmentation rules and pathways of trimethylsilylated small molecules from 80 research articles over the past 5 decades, including diagnostic fragment ions, neutral losses, and typical ion ratios, for alcohols, carboxylic acids, amines, amino acids, sugars, steroids, thiols, and phosphates. These fragmentation rules were subsequently validated by specificity and sensitivity assessments using the NIST 14 nominal mass library and a new in-house GC-QTOF MS library containing 589 accurate mass spectra. From 556 tested fragmentation patterns, 228 rules yielded true positive hits within 4 mDa mass accuracy. These rules can be applied to assign substructures for mass spectra computation and unknown identification. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 9999: XX–XX, 2016.
We describe a tool, Competitive Fragmentation Modeling for Electron Ionization (CFM-EI) that, given a chemical structure (e.g. in SMILES or InChI format), computationally predicts an electron ionization mass spectrum (EI-MS) – i.e. the type of mass spectrum commonly generated by gas chromatography mass spectrometry (GC-MS). The predicted spectra produced by this tool can be used for putative compound identification, complementing measured spectra in reference databases by expanding the range of compounds able to be considered when availability of measured spectra is limited. The tool extends CFM-ESI, a recently developed method for computational prediction of electrospray tandem mass spectra (ESI-MS/MS), but unlike CFM-ESI, CFM-EI can handle odd-electron ions and isotopes, and incorporates an artificial neural network. Tests on EI-MS data from the NIST database demonstrate that CFM-EI is able to model fragmentation likelihoods in low resolution EI-MS data, producing predicted spectra whose dot product scores are significantly better than full enumeration ’bar-code’ spectra. CFM-EI also outperformed previously reported results for MetFrag, MOLGEN-MS and Mass Frontier on one compound identification task. It also outperformed MetFrag in a range of other compound identification tasks involving a much larger data set, containing both derivatized and non-derivatized compounds. While replicate EI-MS measurements of chemical standards are still a more accurate point of comparison, CFM-EI’s predictions provide a much-needed alternative when no reference standard is available for measurement. CFM-EI is available at https://sourceforge.net/projects/cfm-id/ for download and http://cfmid.wishartlab.com as a web service.
The gas phase fragmentation pathways of the nucleobase adenine upon 70 eV electron ionization are investigated by means of a combined stochastic and first principles based molecular dynamics approach. We employ no pre-conceived fragmentation channels in our calculations, which simulate standard electron ionization mass spectrometry (EI-MS) conditions. The reactions observed compare well to a wealth of experimental and theoretical data available for this important nucleic acid building block. All significant peaks in the experimental mass spectrum of adenine are reproduced. Additionally, the fragment ion connectivities obtained from our simulations at least partially concur with results from previous experimental studies on selectively isotope labeled adenines. Moreover, we are able to assign non-cyclic structures which are entropically favored and have not been proposed in non-dynamic quantum chemical studies before to the decomposition products, which result automatically from our molecular dynamics procedure. From simulations under various conditions it is evident that most of the fragmentation reactions even at low internal excess energy (<10 eV) occur very fast within a few ps.
This study presents a showcase for the novel Quantum Chemistry Electron Ionization Mass Spectrometry (QCEIMS) method on five FDA-approved drugs. The method allows a first-principles electronic structure-based prediction of EI mass spectra in principle for any molecule. The systems in this case study are organic substances of nominal masses between 404 and 853 atomic mass units and cover a wide range of functional groups and organic molecular structure motifs. The results demonstrate the widespread applicability of the QCEIMS method for the unbiased computation of EI mass spectra even for larger molecules. Its strengths compared to standard (static) or data base driven approaches in such cases are highlighted. Weak points regarding the required computation times or the approximate character of the employed QC methods are also discussed. We propose QCEIMS as a viable and robust way of predicting EI mass spectra for sizeable organic molecules relevant to medicinal and pharmaceutical chemistry.
The routine calculation of EI mass spectra is based on a combination of fast quantum chemical methods, molecular dynamics, and the stochastic preparation of "hot" primary ions. All basic elementary processes are considered with minor empiricism and realistic potential free energy surfaces are employed. Reasonable spectra are generated along with detailed information on the corresponding decomposition and reaction mechanisms.
The unimolecular dissociation of ionized molecules seldom consists in a direct bond cleavage where the reaction coordinate can be adequately represented by a simple bond stretch. The coordinate which controls the progress of the reaction is not always the reaction coordinate (defined as the degree of freedom whose extension leads to spatial separation of the molecular fragments). The specificity of a dissociation mechanism in a polyatomic species is due to its inherently multidimensional character, i.e. it requires participation of several degrees of freedom. It often consists of a sequence of elementary steps and is therefore controlled by several bottlenecks. The complicated and multistep nature of the reaction mechanism results in a natural tendency towards energy randomization. Radiationless transition from the initial upper electronic states to the ground state of the ion is not always very fast with respect to dissociation. A unimolecular reaction should be seen as a flux in phase space through a critical surface. A transition state corresponds to a surface of least flux, i.e. to a bottleneck of the reaction. For a given elementary step, several may exist, whose positions may vary with energy. The nature of these transition states is not immediately obvious. For instance, they do not necessarily coincide with saddle points or with rotational barriers.
The purpose of this short review is critically to assess the important experiments in gas phase ion chemistry whose correct interpretation can lead to the assigning of structures to organic positive ions. The methods fall into two main categories, (i) the measurement of ion enthalpies and transition state energies for their fragmentations and (ii) the detailed examination of the unimolecular and collision-induced fragmentation behaviour of cations, anions and neutral species. It is argued that in general, none of the above methods alone can suffice for an ion structure determination, but that in combination these techniques provide a powerful tool by means of which ion structures may confidently be assigned.
The basic principles, practices, and pitfalls in the process of compound identification by searching mass spectral reference libraries are presented. Factors affecting the identification process are discussed as members of one of three major contributors to identification confidence: prior probability, risk of false negative results, and risk of false positive results. More general concerns and the problem of "unknown unknowns" are then explored.
Statistical theories of mass spectra are based on two assumptions. The first one, which postulates efficient phase space sampling, is substantiated by various experimentation and is now theoretically much better understood. The efficiency of phase space sampling can be estimated and is found to be quite good. Much effort remains to be done concerning the second assumption. A new impetus should be given to the concept of transition state. A better understanding of the role played by the conservation of angular momentum, the exact significance of transition state switching, and the incorporation of quantum effects are set as goals for the future.
Five algorithms proposed in the literature for library search identification of unknown compounds from their low resolution mass spectra were optimized and tested by matching test spectra against reference spectra in the NIST-EPA-NIH Mass Spectral Database. The algorithms were probability-based matching (PBM), dot-product, Hertz et al. similarity index. Euclidean distance, and absolute value distance. The test set consisted of 12,592 alternate spectra of about 8000 compounds represented in the database. Most algorithms were optimized by varying their mass weighting and intensity scaling factors. Rank in the list of candidate compounds was used as the criterion for accuracy. The best performing algorithm (75% accuracy for rank 1) was the dot-product function that measures the cosine of the angle between spectra represented as vectors. Other methods in order of performance were the Euclidean distance (72%), absolute value distance (68%), PBM (65%), and Hertz et al. (64%). Intensity scaling and mass weighting were important in the optimized algorithms with the square root of the intensity scale nearly optimal and the square or cube the best mass weighting power. Several more complex schemes also were tested, but had little effect on the results. A modest improvement in the performance of the dot-product algorithm was made by adding a term that gave additional weight to relative peak intensities for spectra with many peaks in common.
This article introduces MMFF94, the initial published version of the Merck molecular force field (MMFF). It describes the objectives set for MMFF, the form it takes, and the range of systems to which it applies. This study also outlines the methodology employed in parameterizing MMFF94 and summarizes its performance in reproducing computational and experimental data. Though similar to MM3 in some respects, MMFF94 differs in ways intended to facilitate application to condensed-phase processes in molecular-dynamics simulations. Indeed, MMFF94 seeks to achieve MM3-like accuracy for small molecules in a combined “organic/protein” force field that is equally applicable to proteins and other systems of biological significance. A second distinguishing feature is that the core portion of MMFF94 has primarily been derived from high-quality computational data—ca. 500 molecular structures optimized at the HF/6-31G* level, 475 structures optimized at the MP2/6-31G* level, 380 MP2/6-31G* structures evaluated at a defined approximation to the MP4SDQ/TZP level, and 1450 structures partly derived from MP2/6-31G* geometries and evaluated at the MP2/TZP level. A third distinguishing feature is that MMFF94 has been parameterized for a wide variety of chemical systems of interest to organic and medicial chemists, including many that feature frequently occurring combinations of functional groups for which little, if any, useful experimental data are available. The methodology used in parameterizing MMFF94 represents a fourth distinguishing feature. Rather than using the common “functional group” approach, nearly all MMFF parameters have been determined in a mutually consistent fashion from the full set of available computational data. MMFF94 reproduces the computational data used in its parameterization very well. In addition, MMFF94 reproduces experimental bond lengths (0.014 Å root mean square [rms]), bond angles (1.2° rms), vibrational frequencies (61 cm⁻¹ rms), conformational energies (0.38 kcal/mol/rms), and rotational barriers (0.39 kcal/mol rms) very nearly as well as does MM3 for comparable systems. MMFF94 also describes intermolecular interactions in hydrogen-bonded systems in a way that closely parallels that given by the highly regarded OPLS force field. © 1996 John Wiley & Sons, Inc.
A system has been developed that simulates mass spectra. The modular design allows gradual incorporation of knowledge on mass spectral reaction types. Statistical analyses of instances of such reaction types provide evaluations for the important processes in the mass spectrometer. Such evaluation procedures for the processes of ionization and of alpha-cleavages are presented. A second system has been developed that automatically acquires knowledge about mass spectral reactions directly from experimental mass spectra. It provides a detailed scheme of the individual steps of fragmentations and rearrangements of an organic molecule in the mass spectrometer. Illustrative examples for the interpretation of mass spectra are given.
One of the major obstacles in metabolomics is the identification of unknown metabolites. We tested constraints for reidentifying the correct structures of 29 known metabolite peaks from GCT premier accurate mass chemical ionization GC-TOF mass spectrometry data without any use of mass spectral libraries. Correct elemental formulas were retrieved within the top-3 hits for most molecular ion adducts using the "Seven Golden Rules" algorithm. An average of 514 potential structures per formula was downloaded from the PubChem chemical database and in-silico-derivatized using the ChemAxon software package. After chemical curation, Kovats retention indices (RI) were predicted for up to 747 potential structures per formula using the NIST MS group contribution algorithm and corrected for contribution of trimethylsilyl groups using the Fiehnlib RI library. When matching the range of predicted RI values against the experimentally determined peak retention, all but three incorrect formulas were excluded. For all remaining isomeric structures, accurate mass electron ionization spectra were predicted using the MassFrontier software and scored against experimental spectra. Using a mass error window of 10 ppm for fragment ions, 89% of all isomeric structures were removed and the correct structure was reported in 73% within the top-5 hits of the cases.
MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry (EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MS(n) data of 2337 authentic compounds of metabolites, 11 545 EI-MS and 834 other-MS data of 10,286 volatile natural and synthetic compounds, and 3045 ESI-MS(2) data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI-MS(2) data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS(2) data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI-MS(2) data on an identical compound under different collision-induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data.
The study of the collision-induced dissociation behavior of various substituted isoquinoline-3-carboxamides, which are amongst a group of drug candidates for the treatment of anemic disorders (e.g., FG-2216), allowed for the formulation of the general mechanisms underlying the unusual fragmentation behavior of this class of compounds. Characterization was achieved with high-resolution/high accuracy LTQ-Orbitrap tandem mass spectrometry of the protonated precursor ions. Presented data were substantiated by the synthesis and analysis of proposed gas-phase intermediate structures and stable isotope labeled analogues, as well as by density functional theory calculations. Exemplary, CID of protonated N-[(1-chloro-4-hydroxy-7-isopropoxy-isoquinolin-3-yl)carbonyl]glycine gives rise to the isoquinoline-3-carboxy-methyleneamide product ion which nominally eliminates a fragment of 11 u. This was attributed to the loss of methyleneamine (-29 u) and a concomitant spontaneous and reversible water addition (+18 u) to the resulting acylium ion to yield the protonated isoquinoline-3-carboxylic acid. The same water addition pattern is observed after loss of propylene (-42 u). A further nominal loss of 10 u is explained by the elimination of carbon monoxide (-28 u) followed by another water adduct formation (+18 u) to yield the protonated 1-chloro-3,4,7-trihydroxy-isoquinoline. The source of the multiple gas-phase water addition remained unclear. This atypical fragmentation pattern proved to be highly characteristic for all studied and differentially substituted isoquinoline-3-carboxamides, and offers powerful analytical tools for the establishment of a LC/MS(/MS) based screening procedure for model HIF-stabilizers and their potential metabolites in clinical, forensic and sports drug testing.
Car-Parrinello molecular dynamics (CPMD) studies of neutral (1) and ionized (1 (+.)) valeramide are performed with the aim of providing a rationalization for the unusual temperature effect on the dissociation pattern of 1(+.) observed in mass spectrometric experiments. According to CPMD simulations of neutral valeramide 1 performed at approximately 500 K, the conformation with the fully relaxed carbon backbone predominates (96 %). Conformational changes involving folding of the carbon backbone into conformers that would allow intramolecular H transfers are predicted not to take place spontaneously at this temperature because of the barrier heights associated with these transitions (3.5 and 6.9 kcal mol(-1)), which cannot be overcome by thermal motion alone. For 1(+.), CPMD simulations performed at approximately 300 K reveal a substantial stability of a conformation in which the carbon backbone is fully relaxed; no reaction is observed even after 7 ps. However, when conformers with already folded carbon-backbones are used as initial geometries in the CPMD simulations, the gamma-hydrogen migration (McLafferty rearrangement resulting in C(3)H(6)) is already completed within 2 ps. For this important process, the free activation energy associated with both a required conformational change and the subsequent H transfer equals 4.5 kcal mol(-1), while for the formally related delta-H shift (which eventually gives rise to the elimination of C(2)H(4)/C(2)H(5.)) it amounts to 7.0 kcal mol(-1). Since the barriers associated with conformational changes are energetically more demanding than those of the corresponding hydrogen transfers, 1(+.) is essentially trapped by conformational barriers and long-lived at approximately 300 K. At elevated temperatures (500 K), the preferred reaction (within 7.3 ps) in the CPMD simulation corresponds to the McLafferty rearrangement. The estimated free activation energy associated with this process amounts to 2.5 kcal mol(-1), while the free activation energy for the delta-H transfer equals 4.4 kcal mol(-1). This relatively small free activation energy for the McLafferty rearrangement might cause dissociation of a substantial fraction of 1(+.) prior to the time-delayed mass selection, which would reduce the C3/C2 ratio in the experiments conducted with metastable ions that have a lifetime in the order of some micros at a source temperature of 500 K.
Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most frequently used tools for profiling primary metabolites. Instruments are mature enough to run large sequences of samples; novel advancements increase the breadth of compounds that can be analyzed, and improved algorithms and databases are employed to capture and utilize biologically relevant information. Around half the published reports on metabolite profiling by GC-MS focus on biological problems rather than on methodological advances. Applications span from comprehensive analysis of volatiles to assessment of metabolic fluxes for bioengineering. Method improvements emphasize extraction procedures, evaluations of quality control of GC-MS in comparison to other techniques and approaches to data processing. Two major challenges remain: rapid annotation of unknown peaks; and, integration of biological background knowledge aiding data interpretation.
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