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Toxins
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Toxins and Bioinformatics
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Toxins and Bioinformatics

A special issue of Toxins (ISSN 2072-6651).

Deadline for manuscript submissions: closed (31 August 2018) | Viewed by 8346

Special Issue Editor

Prof. Michal Linial

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Guest Editor
Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Special Issue Information

Dear Colleagues,

One of the main functions of bioinformatics and computational methods is to interpret and give functional meaning to genomic, transcriptomic and proteomic data that are collected in large-scale experiments. Animal toxins are mostly peptides and proteins that appear in various branches of the animal kingdom. They are to be found in the earliest branches of the animal kingdom including sponges and cnidarian (e.g., jellyfish, sea anemones), through mollusks (e.g., cone snails, squids), arthropods (e.g., scorpions, spiders, wasps), vertebrates (e.g., snakes, lizards, frogs) and even mammals (e.g., platypus). We expect to find more toxins and toxin-like proteins in genomes to be sequenced in the future.

This Special Issue of Toxins will explore the advances and limitations of computational methods, algorithms and statistical methods in studying toxins. We seek article that demonstrate the fundamental use of bioinformatics in the study of animal venom/ plant toxins (proteins and peptides). Some specific topics of interest are: (i) databases for toxins classification and annotation; (ii) molecular evolutionary perspective on toxins, ancestral genes and genome dynamics; (iii) protein structure-function relationships as revealed by mutational screens, structural modelling and post-translational modifications; (iv) prediction and discovery tools for overlooked toxins or novel activities. This, however, by no means rules out other topics of interest.

We encourage submissions of novel research, as well as perspective and review articles, on bioinformatics advancing research on toxins.

Prof. Michal Linial
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 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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxins 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

  • Molecular evolution
  • Database
  • Venomics
  • Machine learning
  • Computational Biology Algorithm
  • Mass spectrometry
  • Structural modelling
  • Functional prediction
  • Bioactive peptides

Published Papers (2 papers)

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Research

15 pages, 2082 KiB  
Article
Discovery of Novel Conotoxin Candidates Using Machine Learning
by Qing Li, Maren Watkins, Samuel D. Robinson, Helena Safavi-Hemami and Mark Yandell
Toxins 2018, 10(12), 503; https://0-doi-org.brum.beds.ac.uk/10.3390/toxins10120503 - 01 Dec 2018
Cited by 18 | Viewed by 3620
Abstract
Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, and cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins [...] Read more.
Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, and cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads, and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery, we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5148 new putative conotoxin transcripts that have no homologues in current reference databases. 896 of these were identified by at least three out of four models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families. Full article
(This article belongs to the Special Issue Toxins and Bioinformatics)
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20 pages, 6211 KiB  
Article
Interactions between Triterpenes and a P-I Type Snake Venom Metalloproteinase: Molecular Simulations and Experiments
by Lina María Preciado, Jaime Andrés Pereañez, Ettayapuram Ramaprasad Azhagiya Singam and Jeffrey Comer
Toxins 2018, 10(10), 397; https://0-doi-org.brum.beds.ac.uk/10.3390/toxins10100397 - 28 Sep 2018
Cited by 5 | Viewed by 4194
Abstract
Small molecule inhibitors of snake venom metalloproteinases (SVMPs) could provide a means to rapidly halt the progression of local tissue damage following viperid snake envenomations. In this study, we examine the ability of candidate compounds based on a pentacyclic triterpene skeleton to inhibit [...] Read more.
Small molecule inhibitors of snake venom metalloproteinases (SVMPs) could provide a means to rapidly halt the progression of local tissue damage following viperid snake envenomations. In this study, we examine the ability of candidate compounds based on a pentacyclic triterpene skeleton to inhibit SVMPs. We leverage molecular dynamics simulations to estimate the free energies of the candidate compounds for binding to BaP1, a P-I type SVMP, and compare these results with experimental assays of proteolytic activity inhibition in a homologous enzyme (Batx-I). Both simulation and experiment suggest that betulinic acid is the most active candidate, with the simulations predicting a standard binding free energy of Δ G ∘ = − 11.0 ± 1.4 kcal/mol. The simulations also reveal the atomic interactions that underlie binding between the triterpenic acids and BaP1, most notably the electrostatic interaction between carboxylate groups of the compounds and the zinc cofactor of BaP1. Together, our simulations and experiments suggest that occlusion of the S1 ′ subsite is essential for inhibition of proteolytic activity. While all active compounds make hydrophobic contacts in the S1 ′ site, β -boswellic acid, with its distinct carboxylate position, does not occlude the S1 ′ site in simulation and exhibits negligible activity in experiment. Full article
(This article belongs to the Special Issue Toxins and Bioinformatics)
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