Molecular Formula Identification Research Articles

CFM-ID 4.0 - a web server for accurate MS-based metabolite identification.

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

MFAssignR: Molecular formula assignment software for ultrahigh resolution mass spectrometry analysis of environmental complex mixtures

Ultrahigh resolution mass spectrometry is widely used for nontargeted analysis of complex environmental and biological mixtures, such as dissolved organic matter, due to its unparalleled ability to provide accurate mass measurements. Accurate and efficient characterization of these mixtures is critical to being better able to evaluate their effect on human health and climate. This characterization requires accurate mass signals free from isobaric interferences, instrument noise, and mass measurement biases, allowing for molecular formula identification. To address this need, an open source post-processing pipeline for ultrahigh resolution mass spectra of environmental complex mixtures software was developed. MFAssignR contains functions that perform noise estimation, 13C and 34S polyisotopic mass filtering, mass measurement recalibration, and molecular formula assignment as part of a consistent data processing environment. Novel applications of mass defect analysis were used in the functions for noise estimation and isotope pair identification. Using formula extensions, exact mass measurements are converted to unambiguous molecular formulas via data dependent pathways, reducing a priori decisions. Optional molecular formula ambiguity and multiple non-oxygen heteroatoms are provided for custom user applications, including isotopically labeled reactive species, halogen-containing species, or tandem ultrahigh resolution mass spectrometry. This represents uncommon flexibility for an open-source software package. To evaluate the performance of MFAssignR, it was used to characterize a sample of biomass burning influenced organic aerosol and the results were compared to those from other available methods of molecular formula assignment and noise estimation. The differences between the methods are described here. Overall, the inclusion of a full pipeline of data preparation functions and the data-dependent ambiguity reductions in MFAssignR render excellent results and make MFAssignR well-suited for the consistent and efficient analysis of environmental complex mixtures. MFAssignR is publicly available via GitHub.

Predicting the Presence of Uncommon Elements in Unknown Biomolecules from Isotope Patterns.

The determination of the molecular formula is one of the earliest and most important steps when investigating the chemical nature of an unknown compound. Common approaches use the isotopic pattern of a compound measured using mass spectrometry. Computational methods to determine the molecular formula from this isotopic pattern require a fixed set of elements. Considering all possible elements severely increases running times and more importantly the chance for false positive identifications as the number of candidate formulas for a given target mass rises significantly if the constituting elements are not prefiltered. This negative effect grows stronger for compounds of higher molecular mass as the effect of a single atom on the overall isotopic pattern grows smaller. On the other hand, hand-selected restrictions on this set of elements may prevent the identification of the correct molecular formula. Thus, it is a crucial step to determine the set of elements most likely comprising the compound prior to the assignment of an elemental formula to an exact mass. In this paper, we present a method to determine the presence of certain elements (sulfur, chlorine, bromine, boron, and selenium) in the compound from its (high mass accuracy) isotopic pattern. We limit ourselves to biomolecules, in the sense of products from nature or synthetic products with potential bioactivity. The classifiers developed here predict the presence of an element with a very high sensitivity and high specificity. We evaluate classifiers on three real-world data sets with 663 isotope patterns in total: 184 isotope patterns containing sulfur, 187 containing chlorine, 14 containing bromine, one containing boron, one containing selenium. In no case do we make a false negative prediction; for chlorine, bromine, boron, and selenium, we make ten false positive predictions in total. We also demonstrate the impact of our method on the identification of molecular formulas, in particular on the number of considered candidates and running time. The element prediction will be part of the next SIRIUS release, available from https://bio.informatik.uni-jena.de/software/sirius/ .

Identification, toxicity and control of iodinated disinfection byproducts in cooking with simulated chlor(am)inated tap water and iodized table salt

Chlorine/chloramine residuals are maintained in drinking water distribution systems to prevent microbial contamination and microorganism regrowth. During household cooking processes (e.g., soup making), the residual chlorine/chloramines in tap water may react with the iodide in iodized table salt to form hypoiodous acid, which could react with remaining natural organic matter in tap water and organic matter in food to generate iodinated disinfection byproducts (I-DBPs). However, I-DBPs formed during cooking with chloraminated/chlorinated tap water are almost completely new to researchers. In this work, by adopting precursor ion scan of m/z 127 using ultra performance liquid chromatography/electrospray ionization-triple quadrupole mass spectrometry, many new polar I-DBPs formed during cooking with chloraminated/chlorinated tap water were detected and proposed with structures, of which 3-iodo-4-hydroxybenzaldehyde, 3-iodo-4-hydroxybenzoic acid, 3-iodo-4-hydroxy-5-methylbenzoic acid, diiodoacetic acid, 3,5-diiodo-4-hydroxybenzaldehyde, 3,5-diiodo-4-hydroxybenzoic acid, 2,6-diiodo-4-nitrophenol, 2,4-diiodo-6-nitrophenol, and 2,4,6-triiodophenol were confirmed with standard compounds. With the aid of ultra fast liquid chromatography/ion trap-time of flight-mass spectrometry, molecular formula identification of five new I-DBPs (C8H5O4I, C7H4NO4I, C8H5O5I, C7H4NO5I, and C8H6O3I2) was achieved. A developmental toxicity with a recently developed sensitive bioassay was conducted for the newly identified I-DBPs, suggesting that phenolic I-DBPs (except for iodinated carboxyphenols) were about 50–200 times more developmentally toxic than aliphatic I-DBPs. The major I-DBPs in a baseline simulated cooking water sample were determined to be from 0.72 to 7.63 μg/L. Polar I-DBPs formed under various disinfection and cooking conditions were compared, and suggestions for controlling their formation were provided.

Molecular Formula Identification Using Isotope Pattern Analysis and Calculation of Fragmentation Trees.

We present the results of a fully automated de novo approach for identification of molecular formulas in the CASMI 2013 contest. Only results for Category 1 (molecular formula identification) were submitted. Our approach combines isotope pattern analysis and fragmentation pattern analysis and is completely independent from any (spectral and structural) database. We correctly identified the molecular formula for ten out of twelve challenges, being the best automated method competing in this category.

Atmospheric organic matter in clouds: exact masses and molecular formula identification using ultrahigh-resolution FT-ICR mass spectrometry

Abstract. Clouds alter the composition of atmospheric aerosol by acting as a medium for interactions between gas- and particulate-phase substances. To determine the cloud water atmospheric organic matter (AOM) composition and study the cloud processing of aerosols, two samples of supercooled clouds were collected at the Storm Peak Laboratory near Steamboat Springs, Colorado (3220 m a.s.l.). Approximately 3000 molecular formulas were assigned to ultrahigh-resolution mass spectra of the samples after using a reversed-phase extraction procedure to isolate the AOM components from the cloud water. Nitrogen-containing compounds (CHNO compounds), sulfur-containing compounds (CHOS and CHNOS compounds) and other oxygen-containing compounds (CHO compounds) with molecular weights up to 700 Da were observed. Average oxygen-to-carbon ratios of ∼0.6 indicate a slightly more oxidized composition than most water-soluble organic carbon identified in aerosol studies, which may result from aqueous oxidation in the clouds. The AOM composition indicates significant influences from biogenic secondary organic aerosol (SOA) and residential wood combustion. We observed 60% of the cloud water CHO molecular formulas to be identical to SOA samples of α-pinene, β-pinene, d-limonene, and β-caryophyllene ozonolysis. CHNO compounds had the highest number frequency and relative abundances and are associated with residential wood combustion and NOx oxidation. Multiple nitrogen atoms in the assigned molecular formulas for the nighttime cloud sample composite were observed, indicating the significance of nitrate radical reactions on the AOM composition. Several CHOS and CHNOS compounds with reduced sulfur (in addition to the commonly observed oxidized sulfur-containing compounds) were also observed; however further investigation is needed to determine the origin of the reduced sulfur-containing compounds. Overall, the molecular composition determined using ultrahigh-resolution Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry provides an unambiguous identification of the cloud water organic anion composition in the Rocky Mountain area that could help to improve the understanding of aqueous-phase processes.

Molecular Formula Identification with SIRIUS.

We present results of the SIRIUS2 submission to the 2012 CASMI contest. Only results for Category 1 (molecular formula identification) were submitted. The SIRIUS method and the parameters used are briefly described, followed by detailed analysis of the results and a discussion of cases where SIRIUS2 was unable to come up with the correct molecular formula. SIRIUS2 returns consistently high quality results, with the exception of fragmentation pattern analysis of time-of-flight data. We then discuss possibilities for further improving SIRIUS2 in the future.

Computational mass spectrometry for small molecules.

The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

Molecular Formula Analysis by an MS/MS/MS Technique To Expedite Dereplication of Natural Products

A facile and sensitive mass spectrometric method has been developed for the dereplication of natural products. The method provides information about the molecular formula and substructure of a precursor molecule and its fragments, which are invaluable aids in dereplication of natural products at their early stages of purification and characterization. Collision-induced MS/MS technique is used to fragment a precursor ion into several product ions, and individual product ions are selected and subjected to collision-induced MS/MS/MS analysis. This method enables the identification of the fragmentation pathway of a precursor molecule from its first-generation fragments (MS/MS), through to the nth generation product ions (MSn). It also allows for the identification of the corresponding neutral products released (neutral losses). Elements used in the molecular formula analysis include C, H, N, O, and S, as most natural products are constituted by these five elements. High-resolution mass separation and accurate mass measurements afforded the unique identification of molecular formula of small neutral products. Through sequential add-up of the molecular formulas of the small neutral products, the molecular formula of the precursor ion and its productions were uniquely determined. The molecular formula of the precursor molecule was then reversely used to identify or confirm the molecular formula of the neutral products and that of the productions. The molecular formula of the neutral fragments allowed for the identification of substructures, leading to a rapid and efficient characterization of precursor natural product. The method was applied to paclitaxel (C47H51NO14; 853 amu) to identify its molecular formula and its substructures, and to characterize its potential fragmentation pathways. The method was further validated by correctly identifying the molecular formula of minocycline (C23H27N3O7; 457 amu) and piperacillin (C23H27N5O7S; 517 amu).

Potential analytical applications of interfacing a GC to an FT-ICR MS: fingerprinting complex sample matrixes.

Details of interfacing a high-pressure gas chromatograph to the internal ion source of a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) are described. We present our preliminary results and potential analytical applications of GC/FT-ICR for analyzing complex biological and environmental sample matrixes, such as petroleum mixtures. Based on GC/FT-ICR data, rapid characterization of various automobile gasoline samples is possible. Comparison between acquired data from the GC/FT-ICR MS (in broadband mode) and a commercial GC quadrupole mass spectrometer (QMS) (over a wide mass range) indicates that sensitivity of the GC/FT-ICR MS is an order of magnitude lower. High mass resolution and mass measurement accuracy of FT-ICR MS can be utilized for unambiguous molecular formula identification of unknown analytes.