Basic rules for the interpretation of atmospheric pressure ionization mass spectra of small molecules

This review summarizes the basic rules for the interpretation of atmospheric pressure ionization (API) mass spectra of small molecules written with the style primarily intended for beginners and low-experienced researchers with the mass spectra interpretation. The first and basic step in any interpretation of mass spectra is always the determination of molecular weight, which is relatively easy in case of soft ionization techniques due to the limited extend of fragmentation and the prevailing presence of (de)protonated molecules in the full scan mass spectra. These [M+H]⁺ and [M−H]⁻ ions are often accompanied by low abundant molecular adducts, which can be used as the supplementary information for the unambiguous determination of molecular weights. In certain cases, adduct ions may dominate the spectra. The subsequent interpretation of full scan and tandem mass spectra is more complicated due to a high number of possible functional groups, structural subunits and their combinations resulting in numerous competitive fragmentation pathways. Typical neutral losses and the effect of individual functional groups on the fragmentation are discussed in detail and illustrated with selected examples. Modern mass analyzers have powerful features for the structural elucidation, for example high resolving power, high mass accuracy, multistage tandem mass spectrometry, dedicated softwares for the interpretation of mass spectra and prediction of their fragmentation. Background information on differences among individual ionization techniques suitable for the HPLC–MS coupling and basic types of mass analyzers with consequences for the data interpretation is briefly discussed as well. Selected examples illustrate that the right optimization of chromatographic separation and the use of other than mass spectrometric detectors can bring valuable complementary information.     

A computer search system for chemical structure elucidation based on low-resolution mass spectra

A computerized search system which employs the data on the masses and relative abundances of spectral peaks and primary neutral losses is designed for computer elucidation of chemical structures. Recognition of structural fragments is based on analysis of the structures of reference compounds selected as best matches to the mass spectrum of the compound under investigation. Tests of the system on 67 “unknowns” show that the probability of recognizing a large structural fragment lies in the interval 60–80%, depending on the fragment size (100–50% of molecular weight), and that the reliability of the corresponding structural conclusion is 98%. An approach to automatic selection of the substructure common to all or several of the selected compounds is discussed.     

Neutral losses: A type of important variables in prediction of branching degree for acyclic alkenes from mass spectra

Neutral losses are a type of important variables in mass spectral interpretation. Since it is hard to calculate or extract neutral losses from mass spectra, they are usually discarded. In this study, dissimilarity analysis was employed to extract mass spectral characteristics for predicting branching degree of acyclic alkenes. The relationships between branching degree and neutral loss were constructed under direction of experimental observation and mass spectral fragmentations. A branching degree predictor of acyclic alkenes was subsequently built based on the above relationships. After tested by the experimental data in previous studies, the predictor could correctly provide the branching degree from abundant ions of mass spectra. More importantly, this predictor was able to point out which acyclic alkenes could be predicted correctly or not.     

Chemical substructure identification by mass spectral library searching

A library-search procedure that identifies structural features of an unknown compound from its electron-ionization mass spectrum is described. Like other methods, this procedure first retrieves library compounds whose spectra are most similar to the spectrum of an unknown compound. It then deduces structural features of the unknown compound from the chemical structures of the retrievals. Unlike other methods, the significance of each retrieved spectrum is weighted according to its similarity to the spectrum of the unknown compound. Also, a “peaks-in-common” screening step serves to reduce search times and an optimized dot product function provides the match factor. If the molecular weight of the unknown compound is provided, the identification of certain substructures can be improved by including “neutral loss” peaks. Correlations between the presence of a substructure in a test compound and its presence among library retrievals were derived from the results of searching the NIST/EPA/NIH reference library with a 7891 compound test set. These correlations allow the estimation of probabilities of substructure occurrence and absence in an unknown compound from the results of a library search. This method may be viewed as an optimization of the “K-nearest neighbor” method of Isenhour and co-workers, with improvements that arise from spectrum screening, peak scaling, an optimal distance measure, a relative-distance weighting scheme, and a larger reference library.     

Expertise — an expert system for infrared spectra evaluation

A system for the automated elucidation of chemical structures by the interpretation of infrared spectra is described. “Rules” for the finding of substructure features in an unknown spectrum are obtained by a pattern recognition procedure. A structure generation algorithm links particular substructures, called “superatoms”, together. Fuzzy set operators are used to consider inaccuracies in peak positions and intensities.     

Computer Assisted Structural Interpretation of Mass Spectral Data

An interactive system of programs has been developed to assist in structure elucidation based on mass spectral data. The program relies on algorithms for generating all the structural isomers that constitute alternative explanations of the observed data and it associates relative plausibility values with the different isomers. The structure assembly part of the program allows for the use of overlapping substructural components, such as substructures inferred from the appearance of particular ion patterns in the spectrum of an unknown compound. Mass spectrum interpretation procedures used with this structure assembly process could exploit any form of spectrum-substructure correlation scheme. In this work, the emphasis has been on the use of detailed and class specific spectrum-substructure correlations. Applications of the program are illustrated by means of an example analysis of the mass spectra of a variety of marine sterols.     

Generation of substructure identification rules using feature-combinations from tandem mass spectra

Software to interpret tandem mass spectra, entitled Method for Analyzing Patterns in Spectra (MAPS), has been developed to provide substructure information for an automated compound identification system, This software consists of several program modules which manipulate databases of tandem mass spectra and substructure information, generate substructure identification rules, and apply these rules to the tandem mass spectra of unknown compounds to identify components of their structure. The MAPS rule generation program has been modified to generate rules based on specific combinations of spectral features that occur concertedly. False positives are drastically reduced by searching for “feature-combinations” that have 100% uniqueness with respect to a reference database of compounds. Recall is increased by the determination of multiple feature-combinations indicative of the presence of a given substructure. Strategies were developed in the algorithm for the discovery of feature-combinations that avoid the computation “explosion” that occurs when working with a large number of spectral features. The rules developed have the form: “IF feature-eombination a (FC a) or FC b,..., or FC x, THEN substructure SSn is present.”     

Electrospray ionization-multistage tandem mass spectrometry of complex multitin organometallic compounds

The series of 14 complex organotin(IV) compounds containing many tin atoms and noncovalent bonds in the structure was characterized by electrospray ionization multistage tandem mass spectrometry (ESI-MS(n)). The mass spectra were measured in both polarity modes to obtain complementary structural information. The characteristic pattern of ten natural tin isotopes allowed the determination of the number of tin atoms in the molecular adducts and fragment ions by comparing theoretical and experimental isotopic distributions. Positive ion ESI spectra show unusual adduct formation depending on the type of organic solvent used for the direct infusion analysis owing to the ion-molecule reactions in the ion source. On the basis of the detailed spectral interpretation of organotin(IV) compounds, the fragmentation patterns of multitin organometallic compounds have been proposed. Noncovalent bonds in polymeric complexes are fragmented first, which is then followed by characteristic neutral losses in monomeric units.     

Anharmonicity and Spectra–Structure Correlations in MIR and NIR Spectra of Crystalline Menadione (Vitamin K3)

Mid-infrared (MIR) and near-infrared (NIR) spectra of crystalline menadione (vitamin K₃) were measured and analyzed with aid of quantum chemical calculations. The calculations were carried out using the harmonic approach for the periodic model of crystal lattice and the anharmonic DVPT2 calculations applied for the single molecule model. The theoretical spectra accurately reconstructed the experimental ones permitting for reliable assignment of the MIR and NIR bands. For the first time, a detailed analysis of the NIR spectrum of a molecular system based on a naphthoquinone moiety was performed to elucidate the relationship between the chemical structure of menadione and the origin of the overtones and combination bands. In addition, the importance of these bands during interpretation of the MIR spectrum was demonstrated. The overtones and combination bands contribute to 46.4% of the total intensity of menadione in the range of 3600–2600 cm⁻¹. Evidently, these bands play a key role in shaping of the C-H stretching region of MIR spectrum. We have shown also that the spectral regions without fundamentals may provide valuable structural information. For example, the theoretical calculations reliably reconstructed numerous overtones and combination bands in the 4000–3600 and 2800–1800 cm⁻¹ ranges. These results, provide a comprehensive origin of the fundamentals, overtones and combination bands in the NIR and MIR spectra of menadione, and the relationship of these spectral features with the molecular structure.     

Evaluation of automatically generated substructure identification rules from tandem mass spectra

Substructure identification rules for phenothiazine and barbiturate substructures were generated by using a new version of the Method for Analyzing Patterns in Spectra (MAPS) software. This software uses tandem mass spectra and known substructure content of reference compounds to provide “feature-combination“ rules. A feature-combination is a series of tandem mass spectral features which are completely unique to compounds containing a specified substructure. The current reference databases contain over 11,000 daughter spectra of 100 compounds acquired at two different collision gas pressures (i.e., single- and multiple-collision conditions). The results of rule evaluation procedures are presented and include a comparison of the spectral features developed in rule generation to those identified in documented fragmentation pathways of the indicated substructure. Two potential sources of error due to spectral feature and substructure “cross-correlation“ were identified. If errors occur, they can be detected by calculating cross-correlation coefficients and edited from the rules. A beneficial cross-correlation involving feature-combinations was also discovered. The rules obtained by using single- and multiple-collision data were further evaluated by applying them to tandem mass spectra of 20 test compounds (compounds not in the reference database). The results of these evaluations give a good indication of the utility of the rules for use in an automated structure elucidation system for tandem mass spectrometry data.     

Characterization and Diagnostic Value of Amino Acid Side Chain Neutral Losses Following Electron-Transfer Dissociation

Using a large set of high mass accuracy and resolution ETD tandem mass spectra, we characterized ETD-induced neutral losses. From these data we deduced the chemical formula for 20 of these losses. Many of them have been previously observed in electron-capture dissociation (ECD) spectra, such as losses of the side chains of arginine, aspartic acid, glutamic acid, glutamine, asparagine, leucine, histidine, and carbamidomethylated cysteine residues. With this information, we examined the diagnostic value of these amino acid-specific losses. Among 1285 peptide–spectrum matches, 92.5% have agreement between neutral loss-derived peptide amino acid composition and the peptide sequences. Moreover, we show that peptides can be uniquely identified by using only the accurate precursor mass and amino acid composition based on neutral losses; the median number of sequence candidates from an accurate mass query is reduced from 21 to 8 by adding side chain loss information. Besides increasing confidence in peptide identification, our findings suggest the potential use of these diagnostic losses in ETD spectra to improve false discovery rate estimation and to enhance the performance of scoring functions in database search algorithms.     

Artificial intelligence for the interpretation of combined spectral data : Design and development of a spectrum interpreter

The design of a knowledge-based system for the interpretation of combined spectral data for structure elucidation (EXSPEC) is described. Some basic design features are discussed and the functioning of the knowledge base, inference mechanism and user-interface is outlined. Attention is focussed on the development of a spectrum interpreter for infrared and mass spectral data. Interpretation of spectra for 120 liquid alcohols used for rule generation was successful. The system can be run on a Macintosh II or, more slowly, on a Macintosh Plus.     

Identifying residues in natural organic matter through spectral prediction and pattern matching of 2D NMR datasets

This paper describes procedures for the generation of 2D NMR databases containing spectra predicted from chemical structures. These databases allow flexible searching via chemical structure, substructure or similarity of structure as well as spectral features. In this paper we use the biopolymer lignin as an example. Lignin is an important and relatively recalcitrant structural biopolymer present in the majority of plant biomass. We demonstrate how an accurate 2D NMR database of approximately 600 2D spectra of lignin fragments can be easily constructed, in approximately 2 days, and then subsequently show how some of these fragments can be identified in soil extracts through the use of various search tools and pattern recognition techniques. We demonstrate that once identified in one sample, similar residues are easily determined in other soil extracts. In theory, such an approach can be used for the analysis of any organic mixtures.     

Rapid identification of C21 steroidal saponins in Cynanchum versicolor Bunge by electrospray ionization multi-stage tandem mass spectrometry and liquid chromatography/tandem mass spectrometry

Electrospray ionization multi-stage tandem mass spectrometry (ESI-MSn) and liquid chromatography coupled with on-line electrospray ionization tandem mass spectrometry (LC/ESI-MSn) were performed to elucidate the clearage rule of nine investigated C21 steroidal saponins and identify them in the saponin fraction of 90% ethanolic extracts from the root and rhizome of Cynanchum versicolor Bunge. The fragments of C21 steroidal saponins in positive and negative ESI-MSn were used to deduce their mass spectral fragmentation mechanisms, and their structures were further confirmed by ESI-MSn in positive mode. The MSn spectra of the [M+Na]+ ions for saponins provided a wealth of structural information on glycosidic bond cleavage, which allowed a straightforward interpretation of spectra, with respect to the identifications of features such as the sequences of sugars attached to saponins and sugar type. By using LC/ESI-MSn, nine C21 steroidal saponins were detected in the saponin fraction of C. versicolor, and an isomer of atratoglaucoside A was elucidated simultaneously. All nine compounds showed an abundant ion for the loss of 46 Da (HCOOH) from [M+Na]+. The losses of monosaccharide sequences and aglycone as neutral fragmentation from [M+Na-HCOOH]+ were also acquired as the characteristic ions of these C21 steroidal saponins. It provided important information on monosaccharide sequences and in particular on sugar types and could be used to identify and elucidate other C21 steroidal saponins. These studies allowed us to rapidly identify C21 steroidal saponins from Radix cynanchi atrati. It is indicated that the described method had wide applicability to rapidly screen and provide structural confirmation on C21 steroidal saponins in crude materials.     

Infrared spectra of protonated neurotransmitters: serotonin

The gas-phase IR spectrum of the protonated neurotransmitter serotonin (5-hydroxytryptamine) was measured in the fingerprint range by means of IR multiple photon dissociation (IRMPD) spectroscopy. The IRMPD spectrum was recorded in a Fourier transform ion cyclotron resonance mass spectrometer coupled to an electrospray ionization source and an IR free electron laser. Quantum chemical calculations at the B3LYP and MP2 levels of theory using the cc-pVDZ basis set yield six low-energy isomers in the energy range up to 40 kJ/mol, all of which are protonated at the amino group. Protonation at the indole N atom or the hydroxyl group is substantially less favorable. The IRMPD spectrum is rich in structure and exhibits 22 distinguishable features in the spectral range investigated (530-1885 cm(-1)). The best agreement between the measured IRMPD spectrum and the calculated linear IR absorption spectra is observed for the conformer lowest in energy at both levels of theory, denoted g-1. In this structure, one of the three protons of the ammonium group points toward the indole subunit, thereby maximizing the intramolecular NH(+)-π interaction between the positive charge of the ammonium ion and the aromatic indole ring. This mainly electrostatic cation-π interaction is further stabilized by significant dispersion forces, as suggested by the substantial differences between the DFT and MP2 energies. The IRMPD bands are assigned to individual normal modes of the g-1 conformer, with frequency deviations of less than 29 cm(-1) (average <13 cm(-1)). The effects of protonation on the geometric and electronic structure are revealed by comparison with the corresponding structural, energetic, electronic, and spectroscopic properties of neutral serotonin.     

INFERCNMR: a 13C NMR interpretive library search system

INFERCNMR is an automated (13)C NMR spectrum interpretation aid for use either as a stand-alone program or as a component of a comprehensive, computer-based system for the characterization of chemical structure. The program is an interpretive library search which requires a database of assigned (13)C NMR spectra. An interpretive library search does not require overall structural similarity between an unknown and a library entry in order to retrieve a substructure common to both. Input consists of the chemical shift and one-bond proton-carbon multiplicity of each signal in the spectrum, and the molecular formula of the unknown. Program output is one or more substructures predicted to be present in the unknown, each of which is assigned an estimated prediction accuracy.     

The use of MS classifiers and structure generation to assist in the identification of unknowns in effect-directed analysis

Structure generation and mass spectral classifiers have been incorporated into a new method to gain further information from low-resolution GC-MS spectra and subsequently assist in the identification of toxic compounds isolated using effect-directed fractionation. The method has been developed for the case where little analytical information other than the mass spectrum is available, common, for example, in effect-directed analysis (EDA), where further interpretation of the mass spectra is necessary to gain additional information about unknown peaks in the chromatogram. Structure generation from a molecular formula alone rapidly leads to enormous numbers of structures; hence reduction of these numbers is necessary to focus identification or confirmation efforts. The mass spectral classifiers and structure generation procedure in the program MOLGEN-MS was enhanced by including additional classifier information available from the NIST05 database and incorporation of post-generation ‘filtering criteria’. The presented method can reduce the number of possible structures matching a spectrum by several orders of magnitude, creating much more manageable data sets and increasing the chance of identification. Examples are presented to show how the method can be used to provide ‘lines of evidence’ for the identity of an unknown compound. This method is an alternative to library search of mass spectra and is especially valuable for unknowns where no clear library match is available.     

Rapid identification of fatty acid methyl esters using a multidimensional gas chromatography-mass spectrometry database

A multidimensional approach for the identification of fatty acid methyl esters (FAME) based on GC/MS analysis is described. Mass spectra and retention data of more than 130 FAME from various sources (chain lengths in the range from 4 to 24 carbon atoms) were collected in a database. Hints for the interpretation of FAME mass spectra are given and relevant diagnostic marker ions are deduced indicating specific groups of fatty acids. To verify the identity of single species and to ensure an optimized chromatographic resolution, the database was compiled with retention data libraries acquired on columns of different polarity (HP-5, DB-23, and HP-88). For a combined use of mass spectra and retention data standardized methods of measurement for each of these columns are required. Such master methods were developed and always applied under the conditions of retention time locking (RTL) which allowed an excellent reproducibility and comparability of absolute retention times. Moreover, as a relative retention index system, equivalent chain lengths (ECL) of FAME were determined by linear interpolation. To compare and to predict ECL values by means of structural features, fractional chain lengths (FCL) were calculated and fitted as well. As shown in an example, the use of retention data and mass spectral information together in a database search leads to an improved and reliable identification of FAME (including positional and geometrical isomers) without further derivatizations.     

Post-acquisition ETD spectral processing for increased peptide identifications

Tandem mass spectra (MS/MS) produced using electron transfer dissociation (ETD) differ from those derived from collision-activated dissociation (CAD) in several important ways. Foremost, the predominant fragment ion series are different: c- and z ^·-type ions are favored in ETD spectra while b- and y-type ions comprise the bulk of the fragments in CAD spectra. Additionally, ETD spectra possess charge-reduced precursors and unique neutral losses. Most database search algorithms were designed to analyze CAD spectra, and have only recently been adapted to accommodate c- and z ^·-type ions; therefore, inclusion of these additional spectral features can hinder identification, leading to lower confidence scores and decreased sensitivity. Because of this, it is important to pre-process spectral data before submission to a database search to remove those features that cause complications. Here, we demonstrate the effects of removing these features on the number of unique peptide identifications at a 1% false discovery rate (FDR) using the open mass spectrometry search algorithm (OMSSA). When analyzing two biologic replicates of a yeast protein extract in three total analyses, the number of unique identifications with a ∼1% FDR increased from 4611 to 5931 upon spectral pre-processing—an increase of ∼28. 6%. We outline the most effective pre-processing methods, and provide free software containing these algorithms.