Application of Metabolomics and Integrated Computing in Chemical Biology

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1791

Special Issue Editor


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Guest Editor
Department of Biotechnology and Food Science, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
Interests: computational biochemistry; redox biology; metagenomics; microorganisms; cheminformatics

Special Issue Information

Dear Colleagues,

The use of modeling approaches has culminated in progressive advances in computational and chemical biology, which is envisaged to continuously advance experimental and computational science. Chemical biology studies will continue to be transformed by these advances. While advanced computation skills have long been at the core of training for the physical sciences, they are now becoming critical to the advancement of chemical biology as well. Computational chemical biologists are now leading modern programs that generate and process large datasets in the laboratory and in the field.

This Special Issue will focus on the application of metabolomics and computational modeling to bridge the gap between basic chemical biology and bioinformatics as well as software appreciation, in a way that will enhance ground-breaking discoveries by chemical biologists. It is anticipated that this Special Issue will address ideas that will generate new chemical entities (NCE) and novel active pharmaceutical ingredients (API) against debilitating disorders such as aging, diabetes and its secondary complications, cancer, central nervous system disorders, and microbial diseases, etc. Original research and review articles using computational methods such as molecular docking and advanced molecular dynamic simulations for NCE and API bioprospecting from natural biodiversity, and molecular networking for metabolomics are welcome. Consideration will also be given to papers combining computational and other experimental methods. The overarching objective is to present an up-to-date perspective of this interdisciplinary and insightful field that will benefit chemical biologists and other related fields. This is a Virtual Special Issue and articles will be published as they are accepted and then subsequently compiled in a Special Collection.

Potential topics and themes for consideration include, but are not limited to, the following:

  • Application of metabolomics in quality profiling and characterization of plants and microbes.
  • Bioactive molecule isolation from natural sources (microbes, plants, animals) and molecular docking.
  • Computer-driven NCE/API design from plant and microbial secondary metabolites.
  • Advanced molecular simulations for lead optimization and identification from secondary metabolites.
  • Density functional theory (DFT) calculations for lead compounds.
  • Integrated computational-system-based bioprospecting from natural sources for small molecules identification and development.
  • Computational ligand- and structure-based rationale for API and NCE design.
  • QSAR modeling and applications in bioprospecting from natural sources.
  • Metabolite profiling using molecular networking dereplication methods.

Dr. Saheed Sabiu
Guest Editor

Manuscript Submission Information

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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

  • chemical biology
  • computational biology
  • metabolomics
  • computational modeling
  • new chemical entities (NCE)
  • active pharmaceutical ingredients (API)
  • characterization
  • metabolite profiling

Published Papers (1 paper)

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Research

40 pages, 14702 KiB  
Article
Waste to Medicine: Evidence from Computational Studies on the Modulatory Role of Corn Silk on the Therapeutic Targets Implicated in Type 2 Diabetes Mellitus
by Ayesha Akoonjee, Adedayo Ayodeji Lanrewaju, Fatai Oladunni Balogun, Nokwanda Pearl Makunga and Saheed Sabiu
Biology 2023, 12(12), 1509; https://doi.org/10.3390/biology12121509 - 11 Dec 2023
Cited by 1 | Viewed by 1542
Abstract
Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and/or defective insulin production in the human body. Although the antidiabetic action of corn silk (CS) is well-established, the understanding of the mechanism of action (MoA) behind this potential is lacking. Hence, this [...] Read more.
Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and/or defective insulin production in the human body. Although the antidiabetic action of corn silk (CS) is well-established, the understanding of the mechanism of action (MoA) behind this potential is lacking. Hence, this study aimed to elucidate the MoA in different samples (raw and three extracts: aqueous, hydro-ethanolic, and ethanolic) as a therapeutic agent for the management of T2DM using metabolomic profiling and computational techniques. Ultra-performance liquid chromatography-mass spectrometry (UP-LCMS), in silico techniques, and density functional theory were used for compound identification and to predict the MoA. A total of 110 out of the 128 identified secondary metabolites passed the Lipinski’s rule of five. The Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis revealed the cAMP pathway as the hub signaling pathway, in which ADORA1, HCAR2, and GABBR1 were identified as the key target genes implicated in the pathway. Since gallicynoic acid (−48.74 kcal/mol), dodecanedioc acid (−34.53 kcal/mol), and tetradecanedioc acid (−36.80 kcal/mol) interacted well with ADORA1, HCAR2, and GABBR1, respectively, and are thermodynamically stable in their formed compatible complexes, according to the post-molecular dynamics simulation results, they are suggested as potential drug candidates for T2DM therapy via the maintenance of normal glucose homeostasis and pancreatic β-cell function. Full article
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