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A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Antimicrobial Peptides".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 56446

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Prof. Dr. Agostinho Antunes

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1. CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixoes, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal
2. Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
Interests: genomics (from animals to microorganisms); evolution, molecular ecology; conservation; biotechnology; bioinformatics
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Dr. Guillermin Agüero-Chapin

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Centro Interdisciplinar de Investigação Marinha e Ambiental (CIIMAR), Universidade do Porto, 4099-002 Porto, Portugal
Interests: computational biology; biodiscovery; chemo- and bioinformatics; bioactive peptides; antimicrobial peptides (AMPs); biotechnology
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Dr. Yovani Marrero-Ponce

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cEscuela de Medicina, Colegio de Ciencias de la Salud, Universidad San Francisco de Quito (USFQ), Quito 170157, Pichincha, Ecuador
Interests: drug discovery and molecular modeling; chemo-informatics; molecular descriptors definition for nucleic acids and proteins; molecular similarity and complex networks applied to analyze peptide databases; alignment-free models for protein classification problems
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Special Issue Information

Dear Colleagues,

Bacterial resistance to antibiotics is still a serious concern worldwide, especially nowadays when dealing with bacterial infections associated with COVID-19. Because of the potentialities of the naturally occurring antimicrobial peptides (AMPs) to face the multi-resistant problems, many computational approaches have been developed to assist in the search and design of antibiotic peptides within the AMPs chemical space. Currently, from classical alignment-based (AB) and alignment-free (AF) prediction algorithms to non-conventional approaches such as complex similarity networks are being applied for AMPs detection. On the other hand, the design and optimization of AMPs are also computationally assisted by the in silico generation of both random and rationally oriented peptide libraries. Artificial-intelligence-inspired evolutionary algorithms and models of sequence evolution have supported the optimization of peptide scaffolds and the rational generation of diversity-oriented libraries, respectively.

Last but not least, with the improvement of High-Throughput Screening (HTS) techniques applied to the discovery of AMPs in biological samples, the associated computational approaches have also evolved to assist this biodiscovery process. In this sense, proteogenomic analyses considering both transcriptomic and proteomic data have been successfully applied in the detection of AMPs.

This Special Issue of Antibiotics invites authors to publish original research including in silico approaches used for the rational search/discovery and design of AMPs. Review papers on this topic are welcome, too.

Prof. Dr. Agostinho Antunes
Dr. Guillermin Agüero-Chapin
Dr. Yovani Marrero-Ponce
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. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

rational search and design of AMPs
alignment-based and alignment-free approaches
machine learning
artificial intelligence
biodiscovery with associated computational analyses/tools
non-conventional in silico approaches

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

3 pages, 189 KiB

Open AccessEditorial

A 2022 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides

by Guillermin Agüero-Chapin, Agostinho Antunes and Yovani Marrero-Ponce

Antibiotics 2023, 12(6), 1011; https://doi.org/10.3390/antibiotics12061011 - 5 Jun 2023

Cited by 2 | Viewed by 2469

Abstract

The antimicrobial resistance process has been accelerated by the over-prescription and misuse of antibiotics [...] Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

Research

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17 pages, 3711 KiB

Open AccessArticle

Behind the Curtain: In Silico and In Vitro Experiments Brought to Light New Insights into the Anticryptococcal Action of Synthetic Peptides

by Tawanny K. B. Aguiar, Nilton A. S. Neto, Romério R. S. Silva, Cleverson D. T. Freitas, Felipe P. Mesquita, Luciana M. R. Alencar, Ralph Santos-Oliveira, Gustavo H. Goldman and Pedro F. N. Souza

Antibiotics 2023, 12(1), 153; https://doi.org/10.3390/antibiotics12010153 - 11 Jan 2023

Cited by 4 | Viewed by 1973

Abstract

Cryptococcus neoformans is the pathogen responsible for cryptococcal pneumonia and meningitis, mainly affecting patients with suppressed immune systems. We have previously revealed the mechanism of anticryptococcal action of synthetic antimicrobial peptides (SAMPs). In this study, computational and experimental analyses provide new insights into the mechanisms of action of SAMPs. Computational analysis revealed that peptides interacted with the PHO36 membrane receptor of C. neoformans. Additionally, ROS (reactive oxygen species) overproduction, the enzymes of ROS metabolism, interference in the ergosterol biosynthesis pathway, and decoupling of cytochrome c mitochondrial membrane were evaluated. Three of four peptides were able to interact with the PHO36 receptor, altering its function and leading to ROS overproduction. SAMPs-treated C. neoformans cells showed a decrease in scavenger enzyme activity, supporting ROS accumulation. In the presence of ascorbic acid, an antioxidant agent, SAMPs did not induce ROS accumulation in C. neoformans cells. Interestingly, two SAMPs maintained inhibitory activity and membrane pore formation in C. neoformans cells by a ROS-independent mechanism. Yet, the ergosterol biosynthesis and lactate dehydrogenase activity were affected by SAMPs. In addition, we noticed decoupling of Cyt c from the mitochondria, which led to apoptosis events in the cryptococcal cells. The results presented herein suggest multiple mechanisms imposed by SAMPs against C. neoformans interfering in the development of resistance, thus revealing the potential of SAMPs in treating infections caused by C. neoformans. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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18 pages, 2788 KiB

Open AccessArticle

ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria

by Yasser B. Ruiz-Blanco, Guillermin Agüero-Chapin, Sandra Romero-Molina, Agostinho Antunes, Lia-Raluca Olari, Barbara Spellerberg, Jan Münch and Elsa Sanchez-Garcia

Antibiotics 2022, 11(12), 1708; https://doi.org/10.3390/antibiotics11121708 - 26 Nov 2022

Cited by 9 | Viewed by 19385

Abstract

Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs’ discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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21 pages, 2638 KiB

Open AccessArticle

Novel Alligator Cathelicidin As-CATH8 Demonstrates Anti-Infective Activity against Clinically Relevant and Crocodylian Bacterial Pathogens

by Felix L. Santana, Karel Estrada, Morgan A. Alford, Bing C. Wu, Melanie Dostert, Lucas Pedraz, Noushin Akhoundsadegh, Pavneet Kalsi, Evan F. Haney, Suzana K. Straus, Gerardo Corzo and Robert E. W. Hancock

Antibiotics 2022, 11(11), 1603; https://doi.org/10.3390/antibiotics11111603 - 11 Nov 2022

Cited by 5 | Viewed by 2410

Abstract

Host defense peptides (HDPs) represent an alternative way to address the emergence of antibiotic resistance. Crocodylians are interesting species for the study of these molecules because of their potent immune system, which confers high resistance to infection. Profile hidden Markov models were used to screen the genomes of four crocodylian species for encoded cathelicidins and eighteen novel sequences were identified. Synthetic cathelicidins showed broad spectrum antimicrobial and antibiofilm activity against several clinically important antibiotic-resistant bacteria. In particular, the As-CATH8 cathelicidin showed potent in vitro activity profiles similar to the last-resort antibiotics vancomycin and polymyxin B. In addition, As-CATH8 demonstrated rapid killing of planktonic and biofilm cells, which correlated with its ability to cause cytoplasmic membrane depolarization and permeabilization as well as binding to DNA. As-CATH8 displayed greater antibiofilm activity than the human cathelicidin LL-37 against methicillin-resistant Staphylococcus aureus in a human organoid model of biofilm skin infection. Furthermore, As-CATH8 demonstrated strong antibacterial effects in a murine abscess model of high-density bacterial infections against clinical isolates of S. aureus and Acinetobacter baumannii, two of the most common bacterial species causing skin infections globally. Overall, this work expands the repertoire of cathelicidin peptides known in crocodylians, including one with considerable therapeutic promise for treating common skin infections. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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12 pages, 2807 KiB

Open AccessArticle

Revealing Genome-Based Biosynthetic Potential of Streptomyces sp. BR123 Isolated from Sunflower Rhizosphere with Broad Spectrum Antimicrobial Activity

by Neelma Ashraf, Sana Zafar, Roman Makitrynskyy, Andreas Bechthold, Dieter Spiteller, Lijiang Song, Munir Ahmad Anwar, Andriy Luzhetskyy, Ali Nisar Khan, Kalsoom Akhtar and Shazia Khaliq

Antibiotics 2022, 11(8), 1057; https://doi.org/10.3390/antibiotics11081057 - 4 Aug 2022

Cited by 6 | Viewed by 2940

Abstract

Actinomycetes, most notably the genus Streptomyces, have great importance due to their role in the discovery of new natural products, especially for finding antimicrobial secondary metabolites that are useful in the medicinal science and biotechnology industries. In the current study, a genome-based evaluation of Streptomyces sp. isolate BR123 was analyzed to determine its biosynthetic potential, based on its in vitro antimicrobial activity against a broad range of microbial pathogens, including gram-positive and gram-negative bacteria and fungi. A draft genome sequence of 8.15 Mb of Streptomyces sp. isolate BR123 was attained, containing a GC content of 72.63% and 8103 protein coding genes. Many antimicrobial, antiparasitic, and anticancerous compounds were detected by the presence of multiple biosynthetic gene clusters, which was predicted by in silico analysis. A novel metabolite with a molecular mass of 1271.7773 in positive ion mode was detected through a high-performance liquid chromatography linked with mass spectrometry (HPLC-MS) analysis. In addition, another compound, meridamycin, was also identified through a HPLC-MS analysis. The current study reveals the biosynthetic potential of Streptomyces sp. isolate BR123, with respect to the synthesis of bioactive secondary metabolites through genomic and spectrometric analysis. Moreover, the comparative genome study compared the isolate BR123 with other Streptomyces strains, which may expand the knowledge concerning the mechanism involved in novel antimicrobial metabolite synthesis. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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18 pages, 1485 KiB

Open AccessArticle

Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage

by Diana Lin, Darcy Sutherland, Sambina Islam Aninta, Nathan Louie, Ka Ming Nip, Chenkai Li, Anat Yanai, Lauren Coombe, René L. Warren, Caren C. Helbing, Linda M. N. Hoang and Inanc Birol

Antibiotics 2022, 11(7), 952; https://doi.org/10.3390/antibiotics11070952 - 15 Jul 2022

Cited by 12 | Viewed by 4689

Abstract

Antibiotic resistance is a global health crisis increasing in prevalence every day. To combat this crisis, alternative antimicrobial therapeutics are urgently needed. Antimicrobial peptides (AMPs), a family of short defense proteins, are produced naturally by all organisms and hold great potential as effective alternatives to small molecule antibiotics. Here, we present rAMPage, a scalable bioinformatics discovery platform for identifying AMP sequences from RNA sequencing (RNA-seq) datasets. In our study, we demonstrate the utility and scalability of rAMPage, running it on 84 publicly available RNA-seq datasets from 75 amphibian and insect species—species known to have rich AMP repertoires. Across these datasets, we identified 1137 putative AMPs, 1024 of which were deemed novel by a homology search in cataloged AMPs in public databases. We selected 21 peptide sequences from this set for antimicrobial susceptibility testing against Escherichia coli and Staphylococcus aureus and observed that seven of them have high antimicrobial activity. Our study illustrates how in silico methods such as rAMPage can enable the fast and efficient discovery of novel antimicrobial peptides as an effective first step in the strenuous process of antimicrobial drug development. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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19 pages, 5076 KiB

Open AccessArticle

Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli F_OF₁-ATP Synthase

by Luis Pablo Avila-Barrientos, Luis Fernando Cofas-Vargas, Guillermin Agüero-Chapin, Enrique Hernández-García, Sergio Ruiz-Carmona, Norma A. Valdez-Cruz, Mauricio Trujillo-Roldán, Joachim Weber, Yasser B. Ruiz-Blanco, Xavier Barril and Enrique García-Hernández

Antibiotics 2022, 11(5), 557; https://doi.org/10.3390/antibiotics11050557 - 22 Apr 2022

Cited by 5 | Viewed by 3727

Abstract

With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic β subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the β subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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22 pages, 4027 KiB

Open AccessArticle

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

by Maylin Romero, Yovani Marrero-Ponce, Hortensia Rodríguez, Guillermin Agüero-Chapin, Agostinho Antunes, Longendri Aguilera-Mendoza and Felix Martinez-Rios

Antibiotics 2022, 11(3), 401; https://doi.org/10.3390/antibiotics11030401 - 17 Mar 2022

Cited by 12 | Viewed by 3541

Abstract

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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Review

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19 pages, 2986 KiB

Open AccessReview

Bioactive Peptides against Human Apicomplexan Parasites

by Norma Rivera-Fernández, Jhony Anacleto-Santos, Brenda Casarrubias-Tabarez, Teresa de Jesús López-Pérez, Marcela Rojas-Lemus, Nelly López-Valdez and Teresa I. Fortoul

Antibiotics 2022, 11(11), 1658; https://doi.org/10.3390/antibiotics11111658 - 19 Nov 2022

Cited by 1 | Viewed by 2784

Abstract

Apicomplexan parasites are the causal agents of different medically important diseases, such as toxoplasmosis, cryptosporidiosis, and malaria. Toxoplasmosis is considered a neglected parasitosis, even though it can cause severe cerebral complications and death in immunocompromised patients, including children and pregnant women. Drugs against Toxoplasma gondii, the etiological agent of toxoplasmosis, are highly toxic and lack efficacy in eradicating tissue cysts, promoting the establishment of latent infection and acute relapsing disease. Cryptosporidiosis has been recognized as the most frequent waterborne parasitosis in US outbreaks; anti-cryptosporidium drug discovery still faces a major obstacle: drugs that can act on the epicellular parasite. Severe malaria is most commonly caused by the progression of infection with Plasmodium falciparum. In recent years, great progress has been made in the field of antimalarial drugs and vaccines, although the resistance of P. falciparum to artemisinin has recently gained a foothold in Africa. As seen, the search for new drugs against these parasites remains a challenge. Peptide-based drugs seem to be attractive alternative therapeutic agents recently recognized by the pharmaceutical industry, as they can kill different infectious agents and modulate the immune response. A review of the experimental effects of bioactive peptides on these parasites follows, along with comments. In addition, some biological and metabolomic generalities of the parasites are reviewed to elucidate peptide mechanisms of action on Apicomplexan targets. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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59 pages, 1324 KiB

Open AccessReview

Designed Multifunctional Peptides for Intracellular Targets

by Davor Juretić

Antibiotics 2022, 11(9), 1196; https://doi.org/10.3390/antibiotics11091196 - 3 Sep 2022

Cited by 11 | Viewed by 2971

Abstract

Nature’s way for bioactive peptides is to provide them with several related functions and the ability to cooperate in performing their job. Natural cell-penetrating peptides (CPP), such as penetratins, inspired the design of multifunctional constructs with CPP ability. This review focuses on known and novel peptides that can easily reach intracellular targets with little or no toxicity to mammalian cells. All peptide candidates were evaluated and ranked according to the predictions of low toxicity to mammalian cells and broad-spectrum activity. The final set of the 20 best peptide candidates contains the peptides optimized for cell-penetrating, antimicrobial, anticancer, antiviral, antifungal, and anti-inflammatory activity. Their predicted features are intrinsic disorder and the ability to acquire an amphipathic structure upon contact with membranes or nucleic acids. In conclusion, the review argues for exploring wide-spectrum multifunctionality for novel nontoxic hybrids with cell-penetrating peptides. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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32 pages, 3886 KiB

Open AccessReview

Emerging Computational Approaches for Antimicrobial Peptide Discovery

by Guillermin Agüero-Chapin, Deborah Galpert-Cañizares, Dany Domínguez-Pérez, Yovani Marrero-Ponce, Gisselle Pérez-Machado, Marta Teijeira and Agostinho Antunes

Antibiotics 2022, 11(7), 936; https://doi.org/10.3390/antibiotics11070936 - 13 Jul 2022

Cited by 23 | Viewed by 6036

Abstract

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources. Full article

(This article belongs to the Special Issue Computational Approaches in Discovery & Design of Antimicrobial Peptides)

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