Special Issue "Compound Identification of Small Molecules"

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editor

Dr. Tobias Kind
E-Mail Website
Guest Editor
UC Davis Genome Center, University of California, Davis, CA, USA
Interests: cheminformatics; mass spectrometry; metabolomics

Special Issue Information

Dear Colleagues,

Compound identification in the environmental sciences, drug research and metabolomics is still a challenging topic. Complex matrices contain 5,000 to 10,000 metabolites. Currently, combinations of multiple platforms and analytical techniques can annotate around 1,200 compounds in total. Without the annotation of these unknown metabolites, biological interpretations are impossible.

This Special Issue will deal with a diverse array of methods for structure elucidation, preferably those that can elucidate multiple compounds at once. This can include classical structure elucidation approaches such as NMR, but more importantly those that can perform compound replication based on hyphenated technologies such as GC-MS/MS or LC-MS/MS. Techniques that describe the modelling of physical parameters such as CSS values from ion mobility or retention time and retention index modelling are also invited, even if they are just a gateway to full compound annotation. Chemical derivatization strategies and isotopic labelling strategies are also welcome for this Special Issue, if they can at least elucidate parts of a compound structure or the full structure.

Also welcome are in-silico fragmentation techniques, in-silico spectral modelling techniques and hybrid search strategies that can annotate thousands of unknown spectra. Methods that utilize genomic, transcriptomic or proteomic information that can lead to tentative compound annotations, especially GWAS studies are also highly welcome. Last but not least, molecular and mass spectral database approaches that built the cornerstone of compound replication will fit very well in this issue. 

All papers will be fully peer reviewed to ensure high publications standards. In case of questions it is best to contact the Guest Editor in advance to solve any issues.

Dr. Tobias Kind
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Compound identification and dereplication
  • Computational software approaches
  • Machine learning and quantum chemistry
  • In-silico fragmentation, in-silico modelling of spectra
  • Classical structure elucidation
  • Mass spectrometry, chromatography, ion mobility, NMR
  • GWAS and metabolite identification
  • Molecular and spectral databases
  • In silico expansion of molecular space

Published Papers (5 papers)

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Research

Open AccessArticle
Comprehensive Evaluation of Parameters Affecting One-Step Method for Quantitative Analysis of Fatty Acids in Meat
Metabolites 2019, 9(9), 189; https://doi.org/10.3390/metabo9090189 - 18 Sep 2019
Abstract
Despite various direct transmethylation methods having been published and applied to analysis of meat fatty acid (FA) composition, there are still conflicting ideas about the best method for overcoming all the difficulties posed by analysis of complex mixtures of FA in meat. This [...] Read more.
Despite various direct transmethylation methods having been published and applied to analysis of meat fatty acid (FA) composition, there are still conflicting ideas about the best method for overcoming all the difficulties posed by analysis of complex mixtures of FA in meat. This study performed a systematic investigation of factors affecting a one-step method for quantitative analysis of fatty acids in freeze-dried animal tissue. Approximately 280 reactions, selected using factorial design, were performed to investigate the effect of temperature, reaction time, acid concentration, solvent volume, sample weight and sample moisture. The reaction yield for different types of fatty acids, including saturated, unsaturated (cis, trans and conjugated) and long-chain polyunsaturated fatty acids was determined. The optimised condition for one-step transmethylation was attained with four millilitres 5% sulfuric acid in methanol (as acid catalyst), four millilitres toluene (as co-solvent), 300 mg of freeze-dried meat and incubation at 70 °C for 2 h, with interim mixing by inversion at 30, 60 and 90 min for 15 s. The optimised condition was applied to meat samples from different species, covering a broad range of fat content and offers a simplified and reliable method for analysis of fatty acids from meat samples. Full article
(This article belongs to the Special Issue Compound Identification of Small Molecules)
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Open AccessArticle
Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models
Metabolites 2019, 9(8), 160; https://doi.org/10.3390/metabo9080160 - 01 Aug 2019
Abstract
In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum [...] Read more.
In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum of the unknown molecule, and solving a pre-image problem to find the molecule with the most similar fingerprint. In this paper, we bring forward the following improvements to the IOKR framework: firstly, we formulate the IOKRreverse model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/MS spectrum. Secondly, we introduce an approach to combine several IOKR and IOKRreverse models computed from different input and output kernels, called IOKRfusion. The method is based on minimizing structured Hinge loss of the combined model using a mini-batch stochastic subgradient optimization. Our experiments show a consistent improvement of top-k accuracy both in positive and negative ionization mode data. Full article
(This article belongs to the Special Issue Compound Identification of Small Molecules)
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Open AccessArticle
Simultaneous Synthesis of Vitamins D2, D4, D5, D6, and D7 from Commercially Available Phytosterol, β-Sitosterol, and Identification of Each Vitamin D by HSQC NMR
Metabolites 2019, 9(6), 107; https://doi.org/10.3390/metabo9060107 - 06 Jun 2019
Abstract
We succeeded in simultaneously synthesizing the vitamin D family, vitamins D2, D4, D5, D6, and D7, from β-sitosterol, which is sold as a commercially available reagent from Tokyo Chemical Industry Co., Ltd. It [...] Read more.
We succeeded in simultaneously synthesizing the vitamin D family, vitamins D2, D4, D5, D6, and D7, from β-sitosterol, which is sold as a commercially available reagent from Tokyo Chemical Industry Co., Ltd. It is officially sold as a mixture of four phytosterols {β-sitosterol (40–45%), campesterol (20–30%), stigmasterol, and brassicasterol}. Owing to this, we anticipated that, using this reagent, various vitamin D analogs could be synthesized simultaneously. We also synthesized vitamin D3 from pure cholesterol and analyzed and compared all vitamin D analogs (D2, D3, D4, D5, D6, and D7) by HSQC NMR. We succeeded in clearly demonstrating the difference in the NMR chemical shifts for each vitamin D analog. Full article
(This article belongs to the Special Issue Compound Identification of Small Molecules)
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Open AccessArticle
DynaStI: A Dynamic Retention Time Database for Steroidomics
Metabolites 2019, 9(5), 85; https://doi.org/10.3390/metabo9050085 - 30 Apr 2019
Cited by 1
Abstract
Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with [...] Read more.
Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with comparable physico-chemical properties. Careful tuning of the mobile phase gradient and an appropriate choice of the stationary phase can be used to overcome this problem, in turn modifying the retention times in different ways for each compound. In the usual workflow, this approach is suboptimal for the annotation of features based on retention times since it requires characterizing a library of known compounds for every fine-tuned configuration. We introduce a software solution, DynaStI, that is capable of annotating liquid chromatography-mass spectrometry (LC–MS) features by dynamically generating the retention times from a database containing intrinsic properties of a library of metabolites. DynaStI uses the well-established linear solvent strength (LSS) model for reversed-phase LC. Given a list of LC–MS features and some characteristics of the LC setup, this software computes the corresponding retention times for the internal database and then annotates the features using the exact masses with predicted retention times at the working conditions. DynaStI is able to automatically calibrate its predictions to compensate for deviations in the input parameters. The database also includes identification and structural information for each annotation, such as IUPAC name, CAS number, SMILES string, metabolic pathways, and links to external metabolomic or lipidomic databases. Full article
(This article belongs to the Special Issue Compound Identification of Small Molecules)
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Open AccessArticle
Surfactant Lipidomics of Alveolar Lavage Fluid in Mice Based on Ultra-High-Performance Liquid Chromatography Coupled to Hybrid Quadrupole-Exactive Orbitrap Mass Spectrometry
Metabolites 2019, 9(4), 80; https://doi.org/10.3390/metabo9040080 - 25 Apr 2019
Abstract
Surfactant lipid metabolism is closely related to pulmonary diseases. Lipid metabolism disorder can cause lung diseases, vice versa. With this rationale, a useful method was established in this study to determine the lipidome in bronchoalveolar lavage fluid (BALF) of mice. The lipid components [...] Read more.
Surfactant lipid metabolism is closely related to pulmonary diseases. Lipid metabolism disorder can cause lung diseases, vice versa. With this rationale, a useful method was established in this study to determine the lipidome in bronchoalveolar lavage fluid (BALF) of mice. The lipid components in BALF were extracted by liquid–liquid extraction (methanol and methyl tert-butyl ether, and water). Ultra-high-performance liquid chromatography coupled to hybrid Quadrupole-Exactive Orbitrap mass spectrometry was used to analyze the extracted samples, which showed a broad scanning range of 215–1800 m/z. With MS-DIAL software and built-in LipidBlast database, we identified 38 lipids in positive, and 31 lipids in negative, ion mode, including lysophosphatidylcholine (lysoPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), etc. Then, the changes of lipids in BALF of mice with acute lung injury (ALI) induced by lipopolysaccharide (LPS) was investigated, which may contribute to further exploration of the pathogenesis of ALI. Full article
(This article belongs to the Special Issue Compound Identification of Small Molecules)
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