A novel formalism to characterize the degree of unsaturation of organic molecules

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph

BACKGROUND

Identifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.

RESULTS

Our results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted "MS/MS score", on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.

CONCLUSION

Our method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.

SCIENTIFIC CONTRIBUTION

Developed, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Thermal and Catalytic Decomposition of 2-Hydroxyethylhydrazine and 2-Hydroxyethylhydrazinium Nitrate Ionic Liquid

To develop chemical kinetics models for the combustion of ionic liquid-based monopropellants, identification of the elementary steps in the thermal and catalytic decomposition of components such as 2-hydroxyethylhydrazinium nitrate (HEHN) is needed but is currently not well understood. The first decomposition step in protic ionic liquids such as HEHN is typically the proton transfer from the cation to the anion, resulting in the formation of 2-hydroxyethylhydrazine (HEH) and HNO₃. In the first part of this investigation, the high-temperature thermal decomposition of HEH is probed with flash pyrolysis (<1400 K) and vacuum ultraviolet (10.45 eV) photoionization time-of-flight mass spectrometry (VUV-PI-TOFMS). Next, the investigation into the thermal and catalytic decomposition of HEHN includes two mass spectrometric techniques: (1) tunable VUV-PI-TOFMS (7.4-15 eV) and (2) ambient ionization mass spectrometry utilizing both plasma and laser ionization techniques whereby HEHN is introduced onto a heated inert or iridium catalytic surface and the products are probed. The products can be identified by their masses, their ionization energies, and their collision-induced fragmentation patterns. Formation of product species indicates that catalytic surface recombination is an important reaction process in the decomposition mechanism of HEHN. The products and their possible elementary reaction mechanisms are discussed.

Profiling naphthenic acids in produced water using hollow fiber liquid-phase microextraction combined with gas chromatography coupled to Fourier transform Orbitrap mass spectrometry

In this study, we describe the development of an analytical method to profile naphthenic acids (NAs) from produced water (PW). The NAs were isolated by hollow fiber liquid-phase microextraction (HF-LPME). A microwave-assisted methylation method was used to convert the free acids into its corresponding naphthenic methyl esters (NAMEs). The best reaction conditions were ascertained using central composite design. The optimized sample preparation method exhibited an improved analytical eco-scale value (80 vs. 61) compared to conventional liquid-liquid extraction. Although the primary goal was qualitative analysis of NAMEs (e.g., group-type separation) in produced water, the quantitative performance was also evaluated for future investigations. The instrumental detection and quantification limits were 0.10 ng mL^-1 and 0.16 ng mL^-1, respectively, using full spectrum data acquisition. The accuracy and precision of the proposed method ranged from 90.4 to 96.6 % and 3.3 to 13.1 %, respectively, using matrix-matched working solutions (0.1, 0.5, and 1.0 µg mL^-1). The monoisotopic masses of the adduct ions ([M+H]⁺) and its corresponding fine isotopic patterns were used to determine the elemental composition of the NAMEs in the PW samples. Qualitative analysis indicated the O₂ class as the predominant class in all samples with carbon numbers ranging from C₅ to C₁₉ and double bond equivalent (DBE) values of 1 to 8. Additional classes of polar compounds, i.e., O₃, O₄ and nitrogen-containing classes, are reported for the first time by gas chromatography coupled to Fourier transform Orbitrap mass spectrometry and chemical ionization.

Fragmentation behavior of EDTA complexes under different activation conditions

Ethylenediaminetetraacetic acid (EDTA) is an aminopolycarboxylic acid and complexation agent that is able to bind a large variety of metals. The formation of highly stable metal-EDTA complexes is generally very quick. This has led to the use of EDTA in a variety of applications, including food, medical, and household applications. In the current study, we have investigated the fragmentation behavior of EDTA and various metal complexes under collision-induced dissociation (CID), infrared-multiphoton dissociation (IRMPD), and higher-energy collisional dissociation (HCD) activation conditions. Both, positive and negative mode electrospray ionization (ESI) were applied. The metals used to complex with EDTA ranged from alkaline earth metals, such as sodium and cesium, via calcium, nickel, zinc, aluminum, copper, iron, and indium to yttrium and several lanthanides. Furthermore, the protonated and deprotonated species of EDTA, as well as disodium and trisodium species, have been subjected to fragmentation. The results show that characteristic fragmentations were obtained for EDTA and the metal complexes under the investigated conditions. The use of an ion cyclotron resonance (ICR) and an Orbitrap mass spectrometer, as high resolution-accurate mass instruments, allowed the assignment of elemental compositions undoubtedly for the vast majority of fragments. Certain trends were observed that trend correlated with the size of the metal and the location within the periodic table.

Lipids for Taste masking and Taste assessment in pharmaceutical formulations

Pharmaceutical products often have drawbacks of unacceptable taste and palatability which makes it quite difficult for oral administration to some special populations like pediatrics and geriatrics. To curb this issue different approaches like coating, granulation, extrusion, inclusion complexation, ion-exchange resins, etc for taste masking are employed and among them use of lipids have drawn special attention of researchers. Lipids have a lower melting point which is ideal for incorporating drugs in some of these methods like hot-melt extrusion, melt granulation, spray drying/congealing and emulsification. Lipids play a significant role as a barrier to sustain the release of drugs and biocompatible nature of lipids increases their acceptability by the human body. Further, lipids provide vast opportunities of altering pharmacokinetics of the active ingredients by modulating release profiles. In taste sensors, also known as electronic tongue or e-tongue, lipids are used in preparing taste sensing membranes which are subsequently used in preparing taste sensors. Lipid membrane taste sensors have been widely used in assessing taste and palatability of pharmaceutical and food formulations. This review explores applications of lipids in masking the bitter taste in pharmaceutical formulations and significant role of lipids in evaluation of taste and palatability.

Significant decreases in the volatile organic compound concentration, atmospheric oxidation capacity and photochemical reactivity during the National Day holiday over a suburban site in the North China Plain

To what extent anthropogenic emissions could influence volatile organic compound (VOCs) concentrations and related atmospheric reactivity is still poorly understood. China's 70th National Day holidays, during which anthropogenic emissions were significantly reduced to ensure good air quality on Anniversary Day, provides a unique opportunity to investigate these processes. Atmospheric oxidation capacity (AOC), OH reactivity, secondary transformation, O₃ formation and VOCs-PM_2.5 sensitivity are evaluated based on parameterization methods and simultaneous measurements of VOCs, O₃, NO_x, CO, SO₂, PM_2.5, JO¹D, JNO₂, JNO₃ carried out at a suburban site between Beijing and Tianjin before, during, and after the National Day holiday 2019. During the National Day holidays, the AOC, OH reactivity, O₃ formation potential (OFP) and secondary organic aerosol formation potential (SOAP) were 1.6 × 10⁷ molecules cm^-3 s^-1, 41.8 s^-1, 299.2 μg cm^-3 and 1471.8 μg cm^-3, respectively, which were 42%, 29%, 47% and 42% lower than pre-National Day values and -12%, 42%, 36% and 42% lower than post-National Day values, respectively. Reactions involving OH radicals dominated the AOC during the day, but OH radicals and O₃ reactions at night. Alkanes (the degree of unsaturation = 0, (D, Equation (1)) accounted for the largest contributions to the total VOCs concentration, oxygenated VOCs (OVOCs; D ≤ 1) to OH reactivity and OFP, and aromatics (D = 4) to the SOAP. O₃ production was identified as VOCs-limited by VOCs (ppbC)/NOx (ppbv) ratios during the sampling campaign, with greater VOCs limitation during post- National Day and more-aged air masses during the National Day. The VOCs-sensitivity coefficient (VOCs-S) suggested that VOCs were more sensitive to PM_2.5 in low-pollution domains and during the National Day holiday. This study emphasizes the importance of not only the abundance, reactivity, and secondary transformation of VOCs but also the effects of VOCs on PM_2.5 for the development of effective control strategies to minimize O₃ and PM_2.5 pollution.

Identification of lignin oligomers in Kraft lignin using ultra-high-performance liquid chromatography/high-resolution multiple-stage tandem mass spectrometry (UHPLC/HRMSn)

Kraft lignin is the main source of technically produced lignin. For the development of valuable products based on Kraft lignin, its molecular structure is important. However, the chemical composition of Kraft lignin is still not well known. So far, the analysis of Kraft lignin by mass spectrometry (MS) has been mainly focused on monomeric compounds. Previous MS studies on lignin oligomers (LOs) considered only synthesised LO standards and/or lignins produced by processes other than the Kraft process. Furthermore, published MS methods suffer from using high resolution only in the MS¹ stage in multiple-stage tandem MS methods. A high resolution in all MSⁿ stages would provide more detailed information about LO fragmentation pathways. Since lignin samples are complex mixtures of a large number of similar phenolic compounds, the selection of tentative LOs in the MS data is challenging. In this study, we present a method for non-targeted analysis of LOs in Kraft lignin using ultra-high-performance liquid chromatography/high-resolution multiple-stage tandem mass spectrometry (UHPLC/HRMSⁿ). A pre-selection strategy for LOs has been established based on a data-dependent neutral loss MS³ method in combination with a principal component analysis-quadratic discriminant analysis classification model (PCA-QDA). The method was optimised using a design of experiments (DOE) approach. The developed approach improved the pre-selection of tentative LOs in complex mixtures. From 587 detected peaks, 36 peaks were identified as LOs.

Graphical abstract

Distinct molecular signatures in dissolved organic matter produced by viral lysis of marine cyanobacteria

Dissolved organic matter (DOM) plays a central role in the microbial ecology and biogeochemistry of aquatic environments, yet little is known about how the mechanism of DOM release from its ultimate source, primary producer biomass, affects the molecular composition of the inputs to the dissolved pool. Here we used a model marine phytoplankton, the picocyanobacterium Synechococcus WH7803, to compare the composition of DOM released by three mechanisms: exudation, mechanical cell lysis and infection by the lytic phage S-SM1. A broad, untargeted analytical approach reveals the complexity of this freshly sourced DOM, and comparative analysis between DOM produced by the different mechanisms suggests that exudation and viral lysis are sources of unsaturated, oxygen-rich and possibly novel biomolecules. Furthermore, viral lysis of WH7803 by S-SM1 releases abundant peptides derived from specific proteolysis of the major light-harvesting protein phycoerythrin, raising the possibility that phage infection of these abundant cyanobacteria could be a significant source of high molecular weight dissolved organic nitrogen compounds.

Exploring the novel heterocyclic derivatives as lead molecules for design and development of potent anticancer agents

This paper deals with in silico evaluation of newly proposed heterocyclic derivatives in search of potential anticancer activity. Best possible drug candidates have been proposed using a rational approach employing a pipeline of computational techniques namely MetaPrint2D prediction, molinspiration, cheminformatics, Osiris Data warrior, AutoDock and iGEMDOCK. Lazar toxicity prediction, AdmetSAR predictions, and targeted docking studies were also performed. 27 heterocyclic derivatives were selected for bioactivity prediction and drug likeness score on the basis of Lipinski's rule, Viber rule, Ghose filter, leadlikeness and Pan Assay Interference Compounds (PAINS) rule. Bufuralol, Sunitinib, and Doxorubicin were selected as reference standard drug for the comparison of molecular descriptors and docking. Bufuralol is a known non-selective adreno-receptor blocking agent. Studies showed that beta blockers are also used against different types of cancers. Sunitinib is well known Food and Drug administration (FDA) approved pyrrole containing tyrosine kinase inhibitor and our proposed molecules possess similarities with both drug and doxorubicin is another moiety having anticancer activity. All heterocyclic derivatives were found to obey the drug filters except standard drug Doxorubicin. Bioactivity score of the compounds was predicted for drug targets including enzymes, nuclear receptors, kinase inhibitors, G protein-coupled receptor (GPCR) ligands and ion channel modulators. Absorption, distribution, metabolism and toxicity (ADMET) prediction of all proposed compound showed good Blood-brain barrier (BBB) penetration, Human intestinal absorption (HIA), Caco-2 cell permeability except compound-11 and was found to have no AdmetSAR toxicity as well as carcinogenic effect. Compounds 1-9 were slightly mutagenic while compound 2, 11, 20 and 21 showed carcinogenic effect according to Lazar toxicity prediction. Rests of the compounds were predicted to have no side effect. Molecular docking was performed with vascular endothelial growth factor receptor-2(VEGFR2) and glutathione S-transferase-1 (GSTP1) because both are common cancer causing proteins. Sunitinib and Doxorubicin possess great affinity to inhibit these cancers causing protein. Self-organizing map (SOM) was used to depict data in a simple 2D presentation. Our studies justify that good oral bioavailability and therapeutic efficacy of 10, 12-19 and 22-27 compounds can be considered as potential anticancer agents.

Protic Ionic Liquids for Lignin Extraction-A Lignin Characterization Study

Protic ionic liquids (PILs) have been established as effective solvents for the selective extraction and recovery of lignin from lignocellulosic biomass. In this study, we utilize extensive analytical techniques to characterize the PIL-extracted lignins to (1) expand on the physical/chemical structure, and to (2) develop a better understanding of the mechanism behind the lignin dissolution process. The PIL-lignins were characterized using elemental and FT-IR analyses, alongside molecular weight distribution and chemical modeling via MM2. For the more ionic pyrrolidinium acetate ([Pyrr][Ac]), there is an increase in the fragmentation of lignin, resulting in lignin with a smaller average molecular weight and a more uniform dispersity. This lends better understanding to previous findings indicating that higher ionicity in a PIL leads to increased lignin extraction.

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focused on structural descriptors for use with ML. In this work, the use of chemical descriptors, in addition to structural descriptors, was introduced for adsorption analysis. Evaluation of structural and chemical descriptors coupled with various ML algorithms, including decision tree, Poisson regression, support vector machine and random forest, were carried out to predict methane uptake on hypothetical metal organic frameworks. To highlight their predictive capabilities, ML models were trained on 8% of a data set consisting of 130,398 MOFs and then tested on the remaining 92% to predict methane adsorption capacities. When structural and chemical descriptors were jointly used as ML input, the random forest model with 10-fold cross validation proved to be superior to the other ML approaches, with an R² of 0.98 and a mean absolute percent error of about 7%. The training and prediction using the random forest algorithm for adsorption capacity estimation of all 130,398 MOFs took approximately 2 h on a single personal computer, several orders of magnitude faster than actual molecular simulations on high-performance computing clusters.

Characterization of Polyolefin Pyrolysis Species Produced Under Ambient Conditions by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and Ion Mobility-Mass Spectrometry

Polyolefins such as polyethylene (PE) and polypropylene (PP) are often characterized from their pyrolysis products by Py-MS. Nowadays the development of plasma-based direct probe atmospheric pressure sources allow the direct analysis of these polymers. These sources operate at atmospheric pressure, which implies a limited control of the ionization conditions. It was shown that side reactions could occur with species present in air, such as O_2, which may lead to the formation of oxidized compounds. In this work, ion mobility-mass spectrometry (IM-MS) and Fourier transform ion cyclotron resonance mass spectrometry (FTICR) were used for the exhaustive characterization of the PP and PE pyrolysis ions produced using plasma-based atmospheric pressure ion sources. Both PP and PE yielded distributions of pyrolysis products presenting different amounts of unsaturation but also different numbers of oxygen atoms. In addition, the ions produced from PP presented a lower collision cross-section (CCS) than those produced from PE. In the same way, both PP and PE present repeated patterns separated by 14 m/z in the bidimensional drift time versus m/z plots. Within these plots, several trend lines can be evidenced, which are specific of each polymer investigated. Differences were observed between isotactic and atactic samples concerning the pyrolysis profile relative abundance and collision cross-section. Graphical Abstract ᅟ.

In vitro cleavage of diisocyanate-glutathione conjugates by human gamma-glutamyl transpeptidase-1

Isocyanates differ from many other xenobiotics in their ability to form S-linked conjugates with glutathione (GSH) through direct nucleophilic addition reactions (e.g. without enzymatic "preactivation" and/or transferase activity), potentially predisposing them to metabolism via the mercapturic acid pathway. In vivo, mono-isocyanates are metabolized via the mercapturic acid pathway and excreted as N-acetylated cysteine conjugates, however, the metabolism of di-isocyanates remains unclear. We assessed the ability of purified human gamma-glutamyl transpeptidase-1 (GGT-1), a primary enzyme of the mercapturic acid pathway, to cleave S-linked GSH conjugates of 4,4'-methylene diphenyl diisocyanate (MDI) and 1,6-hexamethylene diisocyanate (HDI), two widely used industrial chemicals. A combination of liquid chromatography (LC), tandem mass spectrometry (MS/MS) and hydrogen-deuterium exchange studies confirmed GGT-1 mediated formation of the 607.2 and 525.2 m/z (M + H)(+) ions corresponding to bis(cys-gly)-MDI and bis(cys-gly)-HDI, respectively, the cleavage products expected from the corresponding bis(GSH)-diisocyanate conjugates. Additional intermediate metabolites and mono(cys-gly)-conjugates with partially hydrolyzed diisocyanate were also observed. Consistent with GGT enzyme kinetics, metabolism proceeded more rapidly under conditions that favored transpeptidation versus hydrolytic mechanisms of cleavage. Together the data demonstrate the capacity of human GGT-1 to cleave GSH conjugates of both aromatic and aliphatic diisocyanates, suggesting a potential role in their metabolism.

Identification of metabolites and thermal transformation products of quinolones in raw cow's milk by liquid chromatography coupled to high-resolution mass spectrometry

The presence of residues of antibiotics, metabolites, and thermal transformation products (TPs), produced during thermal treatment to eliminate pathogenic microorganisms in milk, could represent a risk for people. Cow's milk samples spiked with enrofloxacin (ENR), ciprofloxacin (CIP), difloxacin (DIF), and sarafloxacin (SAR) and milk samples from cows medicated with ENR were submitted to several thermal treatments. The milk samples were analyzed by liquid chromatography-mass spectrometry (LC-MS) to find and identify TPs and metabolites. In this work, 27 TPs of 4 quinolones and 24 metabolites of ENR were found. Some of these compounds had been reported previously, but others were characterized for the first time, including lactose-conjugated CIP, the formamidation reaction for CIP and SAR, and hydroxylation or ketone formation to produce three different isomers for all quinolones studied.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

An aldol-based build/couple/pair strategy for the synthesis of medium- and large-sized rings: discovery of macrocyclic histone deacetylase inhibitors

An aldol-based build/couple/pair (B/C/P) strategy was applied to generate a collection of stereochemically and skeletally diverse small molecules. In the build phase, a series of asymmetric syn- and anti-aldol reactions were performed to produce four stereoisomers of a Boc-protected γ-amino acid. In addition, both stereoisomers of O-PMB-protected alaninol were generated to provide a chiral amine coupling partner. In the couple step, eight stereoisomeric amides were synthesized by coupling the chiral acid and amine building blocks. The amides were subsequently reduced to generate the corresponding secondary amines. In the pair phase, three different reactions were employed to enable intramolecular ring-forming processes: nucleophilic aromatic substitution (S(N)Ar), Huisgen [3+2] cycloaddition, and ring-closing metathesis (RCM). Despite some stereochemical dependencies, the ring-forming reactions were optimized to proceed with good to excellent yields, providing a variety of skeletons ranging in size from 8- to 14-membered rings. Scaffolds resulting from the RCM pairing reaction were diversified on the solid phase to yield a 14 400-membered library of macrolactams. Screening of this library led to the discovery of a novel class of histone deacetylase inhibitors, which display mixed enzyme inhibition, and led to increased levels of acetylation in a primary mouse neuron culture. The development of stereo-structure/activity relationships was made possible by screening all 16 stereoisomers of the macrolactams produced through the aldol-based B/C/P strategy.

Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

Background

Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas.

Results

An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries.

Conclusion

The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website.

Mathematical tools in analytical mass spectrometry

Over the last few decades, mass spectrometry has become a powerful tool for exploring various aspects of molecular processes occurring in biological systems. Such exploration is leading to a greater understanding of various complex life processes; unraveling these processes poses the greatest challenge to contemporary bioscience. With due respect to sample preparation, data analysis is rapidly becoming a major obstacle to the conversion of experimental knowledge into valid conclusions. It is interesting to note that many problems related to mass spectrometry can be solved using techniques from computer science, graph theory and discrete mathematics. The aim of this manuscript is to recollect several essays that demonstrate the power and the need to apply such skills to mass spectrometry data interpretation. Special attention is paid to situations where traditional chemical analysis reaches its limits but mathematical reasoning can still allow us to reach valid conclusions.

Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry

The differences between three different compound classes, natural products, molecules from combinatorial synthesis, and drug molecules, were investigated. The major structural differences between natural and combinatorial compounds originate mainly from properties introduced to make combinatorial synthesis more efficient. These include the number of chiral centers, the prevalence of aromatic rings, the introduction of complex ring systems, and the degree of the saturation of the molecule as well as the number and ratios of different heteroatoms. As drug molecules derive from both natural and synthetic sources, they cover a joint area in property space of natural and combinatorial compounds. A PCA-based scheme is presented that differentiates the three classes of compounds. It is suggested that by mimicking certain distribution properties of natural compounds, combinatorial products might be made that are substantially more diverse and have greater biological relevance.