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Metabolites
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Computational Methods for Secondary Metabolite Discovery Volume 2
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Computational Methods for Secondary Metabolite Discovery Volume 2

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 5358

Special Issue Editors

Dr. Alexey Gurevich

E-Mail Website1 Website2 Website3
Guest Editor
Center for Algorithmic Biotechnology, Saint Petersburg State University, St. Petersburg, Russia
Interests: bioinformatics; chemoinformatics; computational mass spectrometry; metabolomics; natural products; dereplication/annotation; genome/metagenome assembly
Special Issues, Collections and Topics in MDPI journals
Dr. Hosein Mohimani

E-Mail Website
Guest Editor
Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
Interests: computational metabolomics and metagenomics; natural product discovery; microbiome analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Secondary metabolites (SMs) are a rich source of industrially important substances such as medicines and pesticides. The latest breakthroughs in biotechnologies, in particular high-throughput genome sequencing and mass spectrometry, have enabled the acquisition of huge volumes of data from SMs and their producers (bacteria, fungi, and plants). However, the lack of proper computational methods for processing these amounts of data prevents the transformation of SM discovery into a routine pipeline. Four new software addressing this problem were proposed in our previous Special Issue. However, this still seems not to be enough.

This Special Issue is devoted to computational techniques for analyzing metabolomics data. Topics that will be covered by this Special Issue include but are not limited to dereplication/annotation of known SMs in high-resolution mass spectrometry data, discovery of novel compounds using multi-omics approaches, machine learning techniques to increase sensitivity and specificity of SM search tools, genome mining methods to reveal SM biosynthesis, visualization of metabolomics data, and novel databases of SMs. Manuscripts from both software developers and researchers applying existing computational tools to SM discovery are welcome.

Dr. Alexey Gurevich
Dr. Hosein Mohimani
Guest Editors

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 submissions that pass pre-check are 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 2700 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

  • secondary metabolites
  • natural products
  • bioinformatics
  • chemoinformatics
  • computational mass spectrometry
  • genome mining
  • machine learning
  • software
  • algorithms
  • metabolomics data analysis

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Published Papers (2 papers)

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6 pages, 639 KiB  
Article
NPvis: An Interactive Visualizer of Peptidic Natural Product–MS/MS Matches
by Olga Kunyavskaya, Alla Mikheenko and Alexey Gurevich
Metabolites 2022, 12(8), 706; https://doi.org/10.3390/metabo12080706 - 29 Jul 2022
Cited by 1 | Viewed by 2142
Abstract
Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are [...] Read more.
Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are often error-prone and their validation remains a bottleneck. Here, we present NPvis, a visualizer suitable for the evaluation of PNP–MS/MS matches. The tool interactively maps annotated spectrum peaks to the corresponding PNP fragments and allows researchers to assess the match correctness. NPvis accounts for the wide chemical diversity of PNPs that prevents the use of the existing proteomics visualizers. Moreover, NPvis works even if the exact chemical structure of the matching PNP is unknown. The tool is available online and as a standalone application. We hope that it will benefit the community by streamlining PNP data analysis and validation. Full article
(This article belongs to the Special Issue Computational Methods for Secondary Metabolite Discovery Volume 2)
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13 pages, 1484 KiB  
Article
Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation
by Shijinqiu Gao, Hoi Yan Katharine Chau, Kuijun Wang, Hongyu Ao, Rency S. Varghese and Habtom W. Ressom
Metabolites 2022, 12(7), 605; https://doi.org/10.3390/metabo12070605 - 29 Jun 2022
Cited by 5 | Viewed by 2684
Abstract
Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from [...] Read more.
Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated a convolutional neural network (CNN) for molecular fingerprint prediction based on data acquired by MS/MS. We used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20, representing about 36,000 compounds for training and testing our CNN model. The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller). Full article
(This article belongs to the Special Issue Computational Methods for Secondary Metabolite Discovery Volume 2)
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