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Antibiotics
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Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition
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Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 17318

Special Issue Editors

Dr. Guillermin Agüero-Chapin

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Yovani Marrero-Ponce

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Agostinho Antunes

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microbial resistance to antibiotics remains a significant global concern, particularly heightened by the extensive use of antibiotics in the treatment of COVID-19-related infections. Recognizing the potential of antimicrobial peptides (AMPs) in addressing multidrug-resistant issues, several databases and computational approaches have been developed to aid in the discovery and design of antibiotic peptides within the chemical space of the AMPs" Currently, classical alignment-based (AB) and alignment-free (AF) prediction algorithms, and 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.

Finally, 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 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.

Dr. Guillermin Agüero-Chapin
Dr. Yovani Marrero-Ponce
Prof. Dr. Agostinho Antunes
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.

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

  • 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 (6 papers)

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13 pages, 2866 KiB  
Article
Comparative Properties of Helical and Linear Amphipathicity of Peptides Composed of Arginine, Tryptophan, and Valine
by Jessie Klousnitzer, Wenyu Xiang, Vania M. Polynice and Berthony Deslouches
Antibiotics 2024, 13(10), 954; https://doi.org/10.3390/antibiotics13100954 - 11 Oct 2024
Viewed by 1213
Abstract
Background: The persistence of antibiotic resistance has incited a strong interest in the discovery of agents with novel antimicrobial mechanisms. The direct killing of multidrug-resistant bacteria by cationic antimicrobial peptides (AMPs) underscores their importance in the fight against infections associated with antibiotic resistance. [...] Read more.
Background: The persistence of antibiotic resistance has incited a strong interest in the discovery of agents with novel antimicrobial mechanisms. The direct killing of multidrug-resistant bacteria by cationic antimicrobial peptides (AMPs) underscores their importance in the fight against infections associated with antibiotic resistance. Despite a vast body of AMP literature demonstrating a plurality in structural classes, AMP engineering has been largely skewed toward peptides with idealized amphipathic helices (H-amphipathic). In contrast to helical amphipathicity, we designed a series of peptides that display the amphipathic motifs in the primary structure. We previously developed a rational framework for designing AMP libraries of H-amphipathic peptides consisting of Arg, Trp, and Val (H-RWV, with a confirmed helicity up to 88% in the presence of membrane lipids) tested against the most common MDR organisms. Methods: In this study, we re-engineered one of the series of the H-RWV peptides (8, 10, 12, 14, and 16 residues in length) to display the amphipathicity in the primary structure by side-by-side (linear) alignment of the cationic and hydrophobic residues into the 2 separate linear amphipathic (L-amphipathic) motifs. We compared the 2 series of peptides for antibacterial activity, red blood cell (RBC) lysis, killing and membrane-perturbation properties. Results: The L-RWV peptides achieved the highest antibacterial activity at a minimum length of 12 residues (L-RWV12, minimum optimal length or MOL) with the lowest mean MIC of 3–4 µM, whereas the MOL for the H-RWV series was reached at 16 residues (H-RWV16). Overall, H-RWV16 displayed the lowest mean MIC at 2 µM but higher levels of RBC lysis (25–30%), while the L-RWV series displayed minor RBC lytic effects at the test concentrations. Interestingly, when the S. aureus strain SA719 was chosen because of its susceptibility to most of the peptides, none of the L-RWV peptides demonstrated a high level of membrane perturbation determined by propidium iodide incorporation measured by flow cytometry, with <50% PI incorporation for the L-RWV peptides. By contrast, most H-RWV peptides displayed almost up to 100% PI incorporation. The results suggest that membrane perturbation is not the primary killing mechanism of the L-amphipathic RWV peptides, in contrast to the H-RWV peptides. Conclusions: Taken together, the data indicate that both types of amphipathicity may provide different ideal pharmacological properties that deserve further investigation. Full article
(This article belongs to the Special Issue Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition)
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18 pages, 5775 KiB  
Article
dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation
by Min Zhao, Yu Zhang, Maolin Wang and Luyan Z. Ma
Antibiotics 2024, 13(10), 948; https://doi.org/10.3390/antibiotics13100948 - 9 Oct 2024
Viewed by 1611
Abstract
Antibiotic resistance is a growing public health challenge. Antimicrobial peptides (AMPs) effectively target microorganisms through non-specific mechanisms, limiting their ability to develop resistance. Therefore, the prediction and design of new AMPs is crucial. Recently, deep learning has spurred interest in computational approaches to [...] Read more.
Antibiotic resistance is a growing public health challenge. Antimicrobial peptides (AMPs) effectively target microorganisms through non-specific mechanisms, limiting their ability to develop resistance. Therefore, the prediction and design of new AMPs is crucial. Recently, deep learning has spurred interest in computational approaches to peptide drug discovery. This study presents a novel deep learning framework for AMP classification, function prediction, and generation. We developed discoverAMP (dsAMP), a robust AMP predictor using CNN Attention BiLSTM and transfer learning, which outperforms existing classifiers. In addition, dsAMPGAN, a Generative Adversarial Network (GAN)-based model, generates new AMP candidates. Our results demonstrate the superior performance of dsAMP in terms of sensitivity, specificity, Matthew correlation coefficient, accuracy, precision, F1 score, and area under the ROC curve, achieving >95% classification accuracy with transfer learning on a small dataset. Furthermore, dsAMPGAN successfully synthesizes AMPs similar to natural ones, as confirmed by comparisons of physical and chemical properties. This model serves as a reliable tool for the identification of novel AMPs in clinical settings and supports the development of AMPs to effectively combat antibiotic resistance. Full article
(This article belongs to the Special Issue Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition)
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16 pages, 2619 KiB  
Article
Innovative Alignment-Based Method for Antiviral Peptide Prediction
by Daniela de Llano García, Yovani Marrero-Ponce, Guillermin Agüero-Chapin, Francesc J. Ferri, Agostinho Antunes, Felix Martinez-Rios and Hortensia Rodríguez
Antibiotics 2024, 13(8), 768; https://doi.org/10.3390/antibiotics13080768 - 14 Aug 2024
Viewed by 3121
Abstract
Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but [...] Read more.
Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models’ robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew’s correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery. Full article
(This article belongs to the Special Issue Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition)
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23 pages, 8780 KiB  
Article
AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria
by Nisha Bajiya, Shubham Choudhury, Anjali Dhall and Gajendra P. S. Raghava
Antibiotics 2024, 13(2), 168; https://doi.org/10.3390/antibiotics13020168 - 8 Feb 2024
Cited by 7 | Viewed by 4890
Abstract
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed [...] Read more.
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics. Full article
(This article belongs to the Special Issue Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition)
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19 pages, 5758 KiB  
Article
Insights into the Adsorption Mechanisms of the Antimicrobial Peptide CIDEM-501 on Membrane Models
by Daniel Alpízar-Pedraza, Yessica Roque-Diaz, Hilda Garay-Pérez, Frank Rosenau, Ludger Ständker and Vivian Montero-Alejo
Antibiotics 2024, 13(2), 167; https://doi.org/10.3390/antibiotics13020167 - 8 Feb 2024
Cited by 1 | Viewed by 2064
Abstract
CIDEM-501 is a hybrid antimicrobial peptide rationally designed based on the structure of panusin and panulirin template peptides. The new peptide exhibits significant antibacterial activity against multidrug-resistant pathogens (MIC = 2–4 μM) while conserving no toxicity in human cell lines. We conducted molecular [...] Read more.
CIDEM-501 is a hybrid antimicrobial peptide rationally designed based on the structure of panusin and panulirin template peptides. The new peptide exhibits significant antibacterial activity against multidrug-resistant pathogens (MIC = 2–4 μM) while conserving no toxicity in human cell lines. We conducted molecular dynamics (MD) simulations using the CHARMM-36 force field to explore the CIDEM-501 adsorption mechanism with different membrane compositions. Several parameters that characterize these interactions were analyzed to elucidate individual residues’ structural and thermodynamic contributions. The membrane models were constructed using CHARMM-GUI, mimicking the bacterial and eukaryotic phospholipid compositions. Molecular dynamics simulations were conducted over 500 ns, showing rapid and highly stable peptide adsorption to bacterial lipids components rather than the zwitterionic eucaryotic model membrane. A predominant peptide orientation was observed in all models dominated by an electric dipole. The peptide remained parallel to the membrane surface with the center loop oriented to the lipids. Our findings shed light on the antibacterial activity of CIDEM-501 on bacterial membranes and yield insights valuable for designing potent antimicrobial peptides targeting multi- and extreme drug-resistant bacteria. Full article
(This article belongs to the Special Issue Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition)
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22 pages, 8525 KiB  
Article
Computationally Designed AMPs with Antibacterial and Antibiofilm Activity against MDR Acinetobacter baumannii
by Fahad M. Alsaab, Scott N. Dean, Shravani Bobde, Gabriel G. Ascoli and Monique L. van Hoek
Antibiotics 2023, 12(9), 1396; https://doi.org/10.3390/antibiotics12091396 - 1 Sep 2023
Cited by 4 | Viewed by 3281
Abstract
The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we [...] Read more.
The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we utilized a computational AMP design strategy combining database filtering technology plus positional analysis to design a series of novel peptides, named HRZN, designed to be active against A. baumannii. All of the HRZN peptides we synthesized exhibited antimicrobial activity against three MDR A. baumannii strains with HRZN-15 being the most active (MIC 4 µg/mL). This peptide also inhibited and eradicated biofilm of A. baumannii strain AB5075 at 8 and 16 µg/mL, which is highly effective. HRZN-15 permeabilized and depolarized the membrane of AB5075 rapidly, as demonstrated by the killing kinetics. HRZN 13 and 14 peptides had little to no hemolysis activity against human red blood cells, whereas HRZN-15, -16, and -17 peptides demonstrated more significant hemolytic activity. HRZN-15 also demonstrated toxicity to waxworms. Further modification of HRZN-15 could result in a new peptide with an improved toxicity profile. Overall, we successfully designed a set of new AMPs that demonstrated activity against MDR A. baumannii using a computational approach. Full article
(This article belongs to the Special Issue Computational Approaches in Discovery and Design of Antimicrobial Peptides—2nd Edition)
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