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Marine Drugs
Special Issues
Virtual Screening of Marine Natural Products
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Virtual Screening of Marine Natural Products

A special issue of Marine Drugs (ISSN 1660-3397).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10062

Special Issue Editors

Dr. Florbela Pereira

E-Mail Website
Guest Editor
LAQV, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
Interests: chemoinformatics; machine learning and data mining tecniques; quantitative structure–activity relationship (QSAR); quantitative structure–property relationship (QSPR); big data; DFT-calculated properties; marine natural products (MNP); virtual screening; nuclear magnetic resonance (NMR); dereplication; drug discovery
Special Issues, Collections and Topics in MDPI journals
Dr. Rachael Mansbach

E-Mail Website
Guest Editor
Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Interests: molecular simulation; deep learning; protein dynamics

Special Issue Information

Dear Colleagues,                

In recent years, computational methodologies have assisted in the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to reveal mechanisms of action, and to optimize leads.

Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of MNPs either in small datasets of isolated compounds or in large-scale databases.

A comprehensive analysis of available MNP databases, biological and chemical space defined by the known MNPs and the most common LB (e.g., quantitative structure–activity relationships (QSAR), estimation of drug likeness, similarity searching, pharmacophore identification), and SB techniques (e.g., molecular dynamics, docking, binding cavity) in the virtual screening of MNPs are the main focus of this Special Issue.

We invite scientists working in this area to submit their original research or review articles for publication in this Special Issue.

Dr. Florbela Pereira
Dr. Rachael Mansbach
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. Marine Drugs 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 2900 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

  • chemoinformatics
  • machine learning techniques
  • MNP databases
  • chemical space, quantitative structure–activity relationship (QSAR)
  • molecular docking
  • drug design

Published Papers (3 papers)

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Research

17 pages, 6832 KiB  
Article
A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
by Susana P. Gaudêncio and Florbela Pereira
Mar. Drugs 2020, 18(12), 633; https://0-doi-org.brum.beds.ac.uk/10.3390/md18120633 - 10 Dec 2020
Cited by 23 | Viewed by 3812
Abstract
The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main [...] Read more.
The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives. Full article
(This article belongs to the Special Issue Virtual Screening of Marine Natural Products)
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17 pages, 9023 KiB  
Article
Virtual Screening of Marine Natural Compounds by Means of Chemoinformatics and CDFT-Based Computational Peptidology
by Norma Flores-Holguín, Juan Frau and Daniel Glossman-Mitnik
Mar. Drugs 2020, 18(9), 478; https://0-doi-org.brum.beds.ac.uk/10.3390/md18090478 - 20 Sep 2020
Cited by 29 | Viewed by 2866
Abstract
This work presents the results of a computational study of the chemical reactivity and bioactivity properties of the members of the theopapuamides A-D family of marine peptides by making use of our proposed methodology named Computational Peptidology (CP) that has been successfully considered [...] Read more.
This work presents the results of a computational study of the chemical reactivity and bioactivity properties of the members of the theopapuamides A-D family of marine peptides by making use of our proposed methodology named Computational Peptidology (CP) that has been successfully considered in previous studies of this kind of molecular system. CP allows for the determination of the global and local descriptors that come from Conceptual Density Functional Theory (CDFT) that can give an idea about the chemical reactivity properties of the marine natural products under study, which are expected to be related to their bioactivity. At the same time, the validity of the procedure based on the adoption of the KID (Koopmans In DFT) technique, as well as the MN12SX/Def2TZVP/H2O model chemistry is successfully verified. Together with several chemoinformatic tools that can be used to improve the process of virtual screening, some additional properties of these marine peptides are identified related to their ability to behave as useful drugs. With the further objective of analyzing their bioactivity, some useful parameters for future QSAR studies, their predicted biological targets, and the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) parameters related to the theopapuamides A-D pharmacokinetics are also reported. Full article
(This article belongs to the Special Issue Virtual Screening of Marine Natural Products)
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26 pages, 2835 KiB  
Article
Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination
by Rachael A. Mansbach, Srirupa Chakraborty, Timothy Travers and S. Gnanakaran
Mar. Drugs 2020, 18(5), 256; https://0-doi-org.brum.beds.ac.uk/10.3390/md18050256 - 14 May 2020
Cited by 4 | Viewed by 2671
Abstract
Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only [...] Read more.
Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library. Full article
(This article belongs to the Special Issue Virtual Screening of Marine Natural Products)
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