Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

Functional analysis and modification of anti-lipopolysaccharide factor (ALF) from the freshwater crab Sinopotamon henanense and preparation of a novel ShALF6-2 K-AgNPs complex

Overuse of antibiotics has led to the emergence of drug-resistant bacteria and environmental problems. Antimicrobial peptides (AMPs) and silver nanoparticles (AgNPs) can potentially replace antibiotics. Therefore, it is possible to create composite nanostructures with synergistic bactericidal properties by combining AgNPs and AMPs. In this study, a novel anti-lipopolysaccharide factor 6, named ShALF6, was identified in the freshwater crab Sinopotamon henanense. Full-length ShALF6 is 654 bp long and contains a typical lipopolysaccharide-binding domain spanning from Cys51 to Lys72. ShALF6 is highly expressed in hemocytes and responds to infection by the gram-negative bacterium Aeromonas hydrophila. ShALF6 inhibited the growth of gram-negative bacteria by binding to them and disrupting their cell membranes. To alter the charge of ShALF6, the negatively charged glutamic acid (E) in the sequence was replaced with a positively charged lysine (K) and the modified protein was named ShALF6-2 K. The bacteriostatic activity of ShALF6-2 K was significantly enhanced by an increase in the protein's cations. ShALF6-2 K showed high binding efficiency after 36 h of co-incubation with AgNPs and modifying the surface potential of the AgNPs. ShALF6-2 K-AgNPs exhibited synergistic inhibition with enhanced effectiveness against gram-negative bacteria. Finally, the cytotoxicity of ShALF6-2 K-AgNPs was investigated. The combination of ShALF6-2 K and AgNPs significantly reduced the toxic effects of AgNPs on the cells. This study provides theoretical and experimental bases for the development of novel bioactive AMP-coated composite AgNPs.

Extraction of antimicrobial peptides from pea protein hydrolysates by sulfonic acid functionalized biochar

The extraction methods for antimicrobial peptides (AMPs) from plants are varied, but the absence of a standardized and rapid technique remains a challenge. In this study, a functionalized biochar was developed and characterized for the extraction of AMPs from pea protein hydrolysates. The results indicated that the biochar mainly enriched AMPs through electrostatic interaction, hydrogen bonding and pore filling. Then three novel cationic antimicrobial peptides were identified, among which the RDLFK (Arg-Asp-Leu-Phe-Lys) had the greatest inhibitory effect against Staphylococcus aureus and Bacillus subtilis, showcasing IC50 value of 2.372 and 1.000 mg/mL, respectively. Additionally, it was found that RDLFK could damage bacterial cell membranes and penetrate the cells to inhibit DNA synthesis. These results provided that the biochar-based extraction method presents an efficient and promising avenue for isolating AMPs, addressing a critical gap in the current methodologies for their extraction from plant sources.

A review on the diversity of antimicrobial peptides and genome mining strategies for their prediction

Antibiotic resistance has become one of the most serious threats to human health in recent years. In response to the increasing microbial resistance to the antibiotics currently available, it is imperative to develop new antibiotics or explore new approaches to combat antibiotic resistance. Antimicrobial peptides (AMPs) have shown considerable promise in this regard, as the microbes develop low or no resistance against them. The discovery and development of AMPs still confront numerous obstacles such as finding a target, developing assays, and identifying hits and leads, which are time-consuming processes, making it difficult to reach the market. However, with the advent of genome mining, new antibiotics could be discovered efficiently using tools such as BAGEL, antiSMASH, RODEO, etc., providing hope for better treatment of diseases in the future. Computational methods used in genome mining automatically detect and annotate biosynthetic gene clusters in genomic data, making it a useful tool in natural product discovery. This review aims to shed light on the history, diversity, and mechanisms of action of AMPs and the data on new AMPs identified by traditional as well as genome mining strategies. It further substantiates the various phases of clinical trials for some AMPs, as well as an overview of genome mining databases and tools built expressly for AMP discovery. In light of the recent advancements, it is evident that targeted genome mining stands as a beacon of hope, offering immense potential to expedite the discovery of novel antimicrobials.

Bacterial diseases in Siniperca chuatsi: status and therapeutic strategies

Mandarin fish (Siniperca chuatsi) is a prominent freshwater species with significant economic value in China, while disease poses a major hindrance to the advancement of mandarin fish aquaculture. To date, the understanding of the prevention and management of bacterial disease in mandarin fish remains incomplete. Therefore, there is a need for more comprehensive insights into the preventive and curative strategies to address these bacterial infections. In this review, we summarize the information pertaining to the predominant bacterial pathogens such as Aeromonas spp., Flavobacterium columnare, Edwardsiella tarda, Streptococcus uberis and Vibrio cholerae in the mandarin fish aquaculture, and point out the current strategies for diagnosis and combating these bacterial pathogens, as well as deliberate on the prospective alternative treatments such as vaccines, herbal remedies, and phage therapy for the prevention and control of these bacterial diseases. Furthermore, we also highlights the importance to implement an integrated bacterial disease management (IBDM) approach for the prevention and control of these pathogenic bacteria in aquaculture.

Expression and Immune Response Profiles in Nile Tilapia (Oreochromis niloticus) and European Sea Bass (Dicentrarchus labrax) During Pathogen Challenge and Infection

Nile tilapia (Oreochromis niloticus) and European sea bass (Dicentrarchus labrax) are economically significant species in Mediterranean countries, serving essential roles in the aquaculture industry due to high market demand and nutritional value. They experience substantial losses from bacterial pathogens Vibrio anguillarum and Streptococcus iniae, particularly at the onset of the summer season. The immune mechanisms involved in fish infections by V. anguillarum and S. iniae remain poorly understood. This study investigated their impact through experiments with control and V. anguillarum- and S. iniae-infected groups for each species. Blood samples were collected at 1, 3, and 7 days post bacterial injection to assess biochemical and immunological parameters, including enzyme activities (AST and ALT), oxidative markers (SOD, GPX, CAT, and MDA), and leukocyte counts. Further analyses included phagocyte activity, lysozyme activity, IgM levels, and complement C3 and C4 levels. Muscle tissues were sampled at 1, 3, and 7 days post injection to assess mRNA expression levels of 18 immune-relevant genes. The focus was on cytokines and immune-related genes, including pro-inflammatory cytokines (TNF-α, TNF-β, IL-2, IL-6, IL-8, IL-12, and IFN-γ), major histocompatibility complex components (MHC-IIα and MHC-IIβ), cytokine receptors (CXCL-10 and CD4-L2), antimicrobial peptides (Pleurocidin and β-defensin), immune regulatory peptides (Thymosin β12, Leap 2, and Lysozyme g), and Galectins (Galectin-8 and Galectin-9). β-actin was used as the housekeeping gene for normalization. Significant species-specific responses were observed in N. Tilapia and E. Sea Bass when infected with V. anguillarum and S. iniae, highlighting differences in biochemical, immune, and gene expression profiles. Notably, in N. Tilapia, AST levels significantly increased by day 7 during S. iniae infection, reaching 45.00 ± 3.00 (p < 0.05), indicating late-stage acute stress or tissue damage. Conversely, E. Sea Bass exhibited a significant rise in ALT levels by day 7 in the S. iniae group, peaking at 33.5 ± 3.20 (p < 0.05), suggesting liver distress or a systemic inflammatory response. On the immunological front, N. Tilapia showed significant increases in respiratory burst activity on day 1 for both pathogens, with values of 0.28 ± 0.03 for V. anguillarum and 0.25 ± 0.02 for S. iniae (p < 0.05), indicating robust initial immune activation. Finally, the gene expression analysis revealed a pronounced peak of TNF-α in E. Sea Bass by day 7 post V. anguillarum infection with a fold change of 6.120, suggesting a strong species-specific pro-inflammatory response strategy. Understanding these responses provides critical insights for enhancing disease management and productivity in aquaculture operations.

Identification of Multifunctional Putative Bioactive Peptides in the Insect Model Red Palm Weevil (Rhynchophorus ferrugineus)

Innate immunity, the body's initial defense against bacteria, fungi, and viruses, heavily depends on antimicrobial peptides (AMPs), which are small molecules produced by all living organisms. Insects, with their vast biodiversity, are one of the most abundant and innovative sources of AMPs. In this study, AMPs from the red palm weevil (RPW) Rhynchophorus ferrugineus (Coleoptera: Curculionidae), a known invasive pest of palm species, were examined. The AMPs were identified in the transcriptomes from different body parts of male and female adults, under different experimental conditions, including specimens collected from the field and those reared in the laboratory. The RPW transcriptomes were examined to predict antimicrobial activity, and all sequences putatively encoding AMPs were analyzed using several machine learning algorithms available in the CAMPR3 database. Additionally, anticancer, antiviral, and antifungal activity of the peptides were predicted using iACP, AVPpred, and Antifp server tools, respectively. Physicochemical parameters were assessed using the Antimicrobial Peptide Database Calculator and Predictor. From these analyses, 198 putatively active peptides were identified, which can be tested in future studies to validate the in silico predictions. Genome-wide analysis revealed that several AMPs have predominantly emerged through gene duplication. Noticeably, we detect a newly originated defensin allele from an ancestral defensin via the deletion of two amino acids following gene duplication in RPW, which may confer an enhanced resilience to microbial infection. Our study shed light on AMP gene families and shows that high duplication and deletion rates are essential to achieve a diversity of antimicrobial mechanisms; hence, we propose the RPW AMPs as a model for exploring gene duplication and functional variations against microbial infection.

Characterization of the antibacterial and opsonic functions of the antimicrobial peptide LvCrustinVI from Litopenaeus vannamei

Microbial drug resistance is becoming increasingly severe due to antibiotic abuse. The development and utilization of antimicrobial peptides is one of the important ways to solve this difficult problem. Crustins are a family of antimicrobial peptides that play important roles in the innate immune system of crustaceans. Several types of crustins exist in shrimp and their activities vary greatly. In the present study, we studied the immune function of one newly identified crustin and found that the type VI crustin encoding gene in Litopenaeus vannamei (LvCrustinVI) was mainly expressed in gills. Its expression was significantly up-regulated after Vibrio parahaemolyticus infection and knockdown of the gene promoted Vibrio proliferation in the hepatopancreas of shrimp, indicating that LvCrustinVI was involved in pathogens infection. The recombinant LvCrustinVI (rLvCrustinVI) showed strong inhibitory activities against both Gram-negative and Gram-positive bacteria, and exhibited binding activities with the bacteria and bacterial polysaccharides including Glu, LPS and PGN. In the presence of Ca2+, rLvCrustinVI showed a strong agglutination effect on V. parahaemolyticus and could significantly enhance the phagocytic ability of shrimp hemocytes against V. parahaemolyticus. In conclusion, LvCrustinVI played important roles as antimicrobial peptide and opsonin in the innate immune defense of L. vannamei. The study enriched our understanding of the functional activity of Crustin and provides an important basis for the development and utilization of antimicrobial peptides.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Heterologous Production of Antimicrobial Peptides: Notes to Consider

Heavy and irresponsible use of antibiotics in the last century has put selection pressure on the microbes to evolve even faster and develop more resilient strains. In the confrontation with such sometimes called "superbugs", the search for new sources of biochemical antibiotics seems to have reached the limit. In the last two decades, bioactive antimicrobial peptides (AMPs), which are polypeptide chains with less than 100 amino acids, have attracted the attention of many in the control of microbial pathogens, more than the other types of antibiotics. AMPs are groups of components involved in the immune response of many living organisms, and have come to light as new frontiers in fighting with microbes. AMPs are generally produced in minute amounts within organisms; therefore, to address the market, they have to be either produced on a large scale through recombinant DNA technology or to be synthesized via chemical methods. Here, heterologous expression of AMPs within bacterial, fungal, yeast, plants, and insect cells, and points that need to be considered towards their industrialization will be reviewed.

Peptidome Profiling of Bubalus bubalis Urine and Assessment of Its Antimicrobial Activity against Mastitis-Causing Pathogens

Urinary proteins have been studied quite exhaustively in the past, however, the small sized peptides have remained neglected for a long time in dairy cattle. These peptides are the products of systemic protein turnover, which are excreted out of the body and hence can serve as an important biomarker for various pathophysiologies. These peptides in other species of bovine have been reported to possess several bioactive properties. To investigate the urinary peptides in buffalo and simultaneously their bioactivities, we generated a peptidome profile from the urine of Murrah Buffaloes (n = 10). Urine samples were processed using <10 kDa MWCO filter and filtrate obtained was used for peptide extraction using Solid Phase Extraction (SPE). The nLC-MS/MS of the aqueous phase from ten animals resulted in the identification of 8165 peptides originating from 6041 parent proteins. We further analyzed these peptide sequences to identify bioactive peptides and classify them into anti-cancerous, anti-hypertensive, anti-microbial, and anti-inflammatory groups with a special emphasis on antimicrobial properties. With this in mind, we simultaneously conducted experiments to evaluate the antimicrobial properties of urinary aqueous extract on three pathogenic bacterial strains: S. aureus, E. coli, and S. agalactiae. The urinary peptides observed in the study are the result of the activity of possibly 76 proteases. The GO of these proteases showed the significant enrichment of the antibacterial peptide production. The total urinary peptide showed antimicrobial activity against the aforementioned pathogenic bacterial strains with no significant inhibitory effects against a buffalo mammary epithelial cell line. Just like our previous study in cows, the present study suggests the prime role of the antimicrobial peptides in the maintenance of the sterility of the urinary tract in buffalo by virtue of their amino acid composition.

Effects of Antimicrobial Peptides on Antioxidant Properties, Non-specific Immune Response and Gut Microbes of Tsinling Lenok Trout (Brachymystax lenok tsinlingensis)

Antimicrobial peptides (AMPs) are an important part of non-specific immunity and play a key role in the cellular host defense against pathogens and tissue injury infections. We investigated the effects of AMP supplementation on the antioxidant capacity, non-specific immunity, and gut microbiota of tsinling lenok trout. 240 fish were fed diets (CT, A120, A240 and A480) containing different amounts of AMP peptides (0, 120 mg kg-1, 240 mg kg-1, 480 mg kg-1) for 8 weeks. Our results showed that the activity of total antioxidant capacity (T-SOD) and glutathione peroxidase (GSH-Px), lysozyme (LZM), catalase (CAT) and acid phosphatase (ACP) in the A240 and A480 group were higher than that in the CT group (P < 0.05). The content of malondialdehyde (MDA) in AMP group was significantly lower than that in CT group (P < 0.05). Furthermore, we harvested the mid-gut and applied next-generation sequencing of 16S rDNA. The results showed that the abundance of Halomonas in AMP group was significantly lower than that in CT group. Functional analysis showed that the abundance of chloroalkane and chloroalkene degradation pathway increased significantly in AMP group. In conclusion, AMP enhanced the antioxidant capacity, non-specific immunity, and intestinal health of tsinling lenok trout.

Identification of antimicrobial peptide genes from transcriptomes in Mandarin fish (Siniperca chuatsi) and their response to infection with Aeromonas hydrophila

Mandarin fish (Siniperca chuatsi) is a valuable freshwater fish species widely cultured in China. Its aquaculture production is challenged by bacterial septicaemia, which is one of the most common bacterial diseases. Antimicrobial peptides (AMPs) play a critical role in the innate immune system of fish, exhibiting defensive and inhibitory effects against a wide range of pathogens. This study aimed to identify the antimicrobial peptide genes in mandarin fish using transcriptomes data obtained from 17 tissue in our laboratory. Through nucleotide sequence alignment and protein structural domain analysis, 15 antimicrobial peptide genes (moronecidin, pleurocidin, lysozyme g, thymosin β12, hepcidin, leap 2, β-defensin, galectin 8, galectin 9, apoB, apoD, apoE, apoF, apoM, and nk-lysin) were identified, of which 9 antimicrobial peptide genes were identified for the first time. In addition, 15 AMPs were subjected to sequence characterization and protein structure analysis. After injection with Aeromonas hydrophila, the number of red blood cells, hemoglobin concentration, and platelet counts in mandarin fish showed a decreasing trend, indicating partial hemolysis. The expression change patterns of 15 AMP genes in the intestine after A. hydrophila infection were examined by using qRT-PCR. The results revealed, marked up-regulation (approximately 116.04) of the hepcidin gene, down-regulation of the piscidin family genes expression. Moreover, most AMP genes were responded in the early stages after A. hydrophila challenge. This study provides fundamental information for investigating the role of the different antimicrobial peptide genes in mandarin fish in defense against A. hydrophila infection.

Geometric deep learning as a potential tool for antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.

The biological role of charge distribution in linear antimicrobial peptides

INTRODUCTION

Antimicrobial peptides (AMP) have received particular attention due to their capacity to kill bacteria. Although much is known about them, peptides are currently being further researched. A large number of AMPs have been discovered, but only a few have been approved for topical use, due to their promiscuity and other challenges, which need to be overcome.

AREAS COVERED

AMPs are diverse in structure. Consequently, they have varied action mechanisms when targeting microorganisms or eukaryotic cells. Herein, the authors focus on linear peptides, particularly those that are alpha-helical structured, and examine how their charge distribution and hydrophobic amino acids could modulate their biological activity.

EXPERT OPINION

The world currently needs urgent solutions to the infective problems caused by resistant pathogens. In order to start the race for antimicrobial development from the charge distribution viewpoint, bioinformatic tools will be necessary. Currently, there is no software available that allows to discriminate charge distribution in AMPs and predicts the biological effects of this event. Furthermore, there is no software available that predicts the side-chain length of residues and its role in biological functions. More specialized software is necessary.

Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack of new commercially available antibiotics have made in silico discovery of AMPs increasingly important. Machine learning (ML) has improved the speed and efficiency of AMP discovery while reducing the cost of experimental approaches. Despite various ML platforms developed, there is still a lack of integrative use of ML platforms for AMP discovery from publicly available protein databases. Therefore, our study aims to screen potential AMPs with antibiofilm properties from databases using ML platforms, followed by protein-peptide molecular docking analysis and molecular dynamics (MD) simulations. A total of 5850 peptides classified as non-AMP were screened from UniProtKB and analyzed using various online ML platforms (e.g., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). Eight potential AMP peptides against Klebsiella pneumoniae with antibiofilm, non-toxic and non-hemolytic properties were then docked to MrkH, a transcriptional regulator of type 3 fimbriae involved in biofilm formation. Five of eight peptides bound more strongly than the native MrkH ligand when analyzed using HADDOCK and HPEPDOCK. Following the docking studies, our MD simulated that a Neuropeptide B (Peptide 3) bind strongly to the MrkH active sites. The discovery of putative AMPs that exceed the binding energies of the native ligand underscores the utility of the combined ML and molecular simulation strategies for discovering novel AMPs with antibiofilm properties.

In Silico Prospecting for Novel Bioactive Peptides from Seafoods: A Case Study on Pacific Oyster (Crassostrea gigas)

Pacific oyster (Crassostrea gigas), an abundant bivalve consumed across the Pacific, is known to possess a wide range of bioactivities. While there has been some work on its bioactive hydrolysates, the discovery of bioactive peptides (BAPs) remains limited due to the resource-intensive nature of the existing discovery pipeline. To overcome this constraint, in silico-based prospecting is employed to accelerate BAP discovery. Major oyster proteins were digested virtually under a simulated gastrointestinal condition to generate virtual peptide products that were screened against existing databases for peptide bioactivities, toxicity, bitterness, stability in the intestine and in the blood, and novelty. Five peptide candidates were shortlisted showing antidiabetic, anti-inflammatory, antihypertensive, antimicrobial, and anticancer potential. By employing this approach, oyster BAPs were identified at a faster rate, with a wider applicability reach. With the growing market for peptide-based nutraceuticals, this provides an efficient workflow for candidate scouting and end-use investigation for targeted functional product preparation.

Antimicrobial peptides with anticancer activity: Today status, trends and their computational design

Some antimicrobial peptides have been shown to be able to inhibit the proliferation of cancer cell lines. Various strategies for treating cancers with active peptides have been pursued. According to the reports, anticancer peptides are important therapeutic peptides, which can act through two distinct pathways: they either just create pores in the cell membrane, or they have a vital intracellular target. In this review, publications up to Sep. 2021 had extracted form Scopus and PubMed using "antimicrobial peptide" and "anticancer peptide" as keywords. In second step, "computational design" related publications extracted. Among publications, those have similar scopes were classified and selected based on mechanisms of action and application. In this review, the most recent advances in the field of antimicrobial peptides with anti-cancer activities have been summarized. Freely available webservers such as AntiCP, ACPP, iACP, iACP-GAEnsC, ACPred are discussed here. In conclusion, despite some limitations of ACPs such as production cost and challenges, short half-life and toxicity on normal cells, the beneficial properties of AMPs make some of them good therapeutic agents for cancer therapy. Towards designing novel ACPs, the computational methods have substantial position and have been used progressively, today.

Short Antimicrobial Peptides: Therapeutic Potential and Recent Advancements

There has been a lot of interest in antimicrobial peptides (AMPs) as potential next-generation antibiotics. They are components of the innate immune system. AMPs have broad-spectrum action and are less prone to resistance development. They show potential applications in various fields, including medicine, agriculture, and the food industry. However, despite the good activity and safety profiles, AMPs have had difficulty finding success in the clinic due to their various limitations, such as production cost, proteolytic susceptibility, and oral bioavailability. To overcome these flaws, a number of solutions have been devised, one of which is developing short antimicrobial peptides. Short antimicrobial peptides do have an advantage over longer peptides as they are more stable and do not collapse during absorption. They have generated a lot of interest because of their evolutionary success and advantageous properties, such as low molecular weight, selective targets, cell or organelles with minimal toxicity, and enormous therapeutic potential. This article provides an overview of the development of short antimicrobial peptides with an emphasis on those with ≤ 30 amino acid residues as a potential therapeutic agent to fight drug-resistant microorganisms. It also emphasizes their applications in many fields and discusses their current state in clinical trials.

ACP_MS: prediction of anticancer peptides based on feature extraction

Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.

Antibacterial and Anti-Inflammatory Properties of Peptide KN-17

Peri-implantitis, an infectious disease originating from dental biofilm that forms around dental implants, which causes the loss of both osseointegration and bone tissue. KN-17, a truncated cecropin B peptide, demonstrated efficacy against certain bacterial strains associated with peri-implantitis. This study aimed to assess the antibacterial and anti-inflammatory properties and mechanisms of KN-17. The effects of KN-17 on oral pathogenic bacteria were assessed by measuring its minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Moreover, the cytotoxicity and anti-inflammatory effects of KN-17 were evaluated. KN-17 inhibited the growth of Streptococcus gordonii and Fusobacterium nucleatum during in vitro biofilm formation and possessed low toxicity to hBMSCs cells. KN-17 also caused RAW264.7 macrophages to transform from M1 to M2 by downregulating pro-inflammatory and upregulating anti-inflammatory factors. It inhibited the NF-κB signaling pathway by reducing IκBα and P65 protein phosphorylation while promoting IκBα degradation and nuclear P65 translocation. KN-17 might be an efficacious prophylaxis against peri-implant inflammation.

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Recent Advances in Multifunctional Antimicrobial Peptides as Immunomodulatory and Anticancer Therapy: Chromogranin A-Derived Peptides and Dermaseptins as Endogenous versus Exogenous Actors

Antimicrobial peptides (AMPs) are produced by all living organisms exhibiting antimicrobial activities and representing the first line of innate defense against pathogens. In this context, AMPs are suggested as an alternative to classical antibiotics. However, several researchers reported their involvement in different processes defining them as Multifunctional AMPs (MF-AMPs). Interestingly, these agents act as the endogenous responses of the human organism against several dangerous stimuli. Still, they are identified in other organisms and evaluated for their anticancer therapy. Chromogranin A (CgA) is a glyco-phosphoprotein discovered for the first time in the adrenal medulla but also produced in several cells. CgA can generate different derived AMPs influencing numerous physiological processes. Dermaseptins (DRSs) are a family of α-helical-shaped polycationic peptides isolated from the skin secretions of several leaf frogs from the Phyllomedusidae family. Several DRSs were identified as AMPs and, until now, more than 65 DRSs have been classified. Recently, these exogenous molecules were characterized for their anticancer activity. In this review, we summarize the role of these two classes of MF-AMPs as an example of endogenous molecules for CgA-derived peptides, able to modulate inflammation but also as exogenous molecules for DRSs, exerting anticancer activities.

Metal-Ion Interactions with Dodecapeptide Fragments of Human Cationic Antimicrobial Protein LL-37 [hCAP(134-170)]

Isothermal titration calorimetry, circular dichroism (CD) techniques, and in silico analysis were used to determine potential metal binding sites in human cationic antimicrobial protein (hCAP) corresponding to overlapping the dodecapeptide sequences of hCAP(134-170) referred to as LL-37. The correct antibacterial action of LL-37 is closely related to its established unique structure. Disturbances in the LL-37 structure (e.g., unwanted presence of metal ions) lead to a radical change in its biological functions. Five fragments of the LL-37 [hCAP(134-170)], namely, hCAP(134-145) (A1), hCAP(140-151) (A2), hCAP(146-157) (A3), hCAP(152-163) (A4), and hCAP(159-170) (A5), were taken into account and their affinity to Mn(II) and Zn(II) ions was rigorously assessed. We prove that only three of the investigated peptides (A1, A2, and A5) are capable of forming thermodynamically stable complexes with metal ions. Additionally, based on density functional theory (DFT) calculations, we propose the most likely coordination modes of metal(II) to peptides as well as discuss the chemical nature of the interactions. Finally, we present the structural features of the strongest binding peptide, hCAP(159-170), responsible for the metal binding. The presented results provide important structural and thermodynamic information to understand the influence of some metal ions on the activity of hCAP(134-170).

Multidrug-Resistant Microbial Therapy Using Antimicrobial Peptides and the CRISPR/Cas9 System

The emergence and spread of multidrug-resistant microbes become a serious threat to animal and human health globally because of their less responsiveness to conventional antimicrobial therapy. Multidrug-resistant microbial infection poses higher morbidity and mortality rate with significant economic losses. Currently, antimicrobial peptides and the CRISPR/Cas9 system are explored as alternative therapy to circumvent the challenges of multidrug-resistant organisms. Antimicrobial peptides are small molecular weight, cationic peptides extracted from all living organisms. It is a promising drug candidate for the treatment of multidrug-resistant microbes by direct microbial killing or indirectly modulating the innate immune system. The CRISPR/Cas9 system is another novel antimicrobial alternative used to manage multidrug-resistant microbial infection. It is a versatile gene-editing tool that uses engineered single guide RNA for targeted gene recognition and the Cas9 enzyme for the destruction of target nucleic acids. Both the CRISPR/Cas9 system and antimicrobial peptides were used to successfully treat nosocomial infections caused by ESKAPE pathogens, which developed resistance to various antimicrobials. Despite, their valuable roles in multidrug-resistant microbial treatments, both the antimicrobial peptides and the CRISPR/Cas systems have various limitations like toxicity, instability, and incurring high manufacturing costs. Thus, this review paper gives detailed explanations of the roles of the CRISPR/Cas9 system and antimicrobial peptides in circumventing the challenges of multidrug-resistant microbial infections, its limitation and prospects in clinical applications.

Antibacterial activity of an anti-lipopolysaccharide factor (MjALF-D) identified from kuruma prawn (Marsupenaeus japonicus)

Antimicrobial peptides (AMPs) play important roles in host innate immune systems. Anti-lipopolysaccharide factor (ALF), which is a primary AMP in crustaceans, is active against bacteria, fungi and some viruses. MjALF-D, an anionic peptide, is a group D ALF isolated from Marsupenaeus japonicus. In the present study, a series of experiments were performed to study its antibacterial spectrum and further explore its antibacterial and bacterial binding activities. Liquid growth inhibition data demonstrated that recombinant MjALF-D (rMjALF-D) possessed strong antibacterial activity against the gram-positive bacterium Micrococcus luteus and the gram-negative bacterium Photobacterium damselae, with a minimum inhibitory concentration (MIC) or minimum bactericidal concentration (MBC) lower than 1.25 μM. The kinetic analysis showed that the antibacterial activity of rMjALF-D was dose- and time-dependent. Additionally, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) observations the potential bactericidal process. rMjALF-D treatment resulted in a large number of unidentified filamentous structures wrapped around the bacteria, and during the incubation, the cell surface became obviously rough and disrupted. rMjALF-D showed distinct binding ability after direct incubation with M. luteus and P. damselae but no binding ability to Escherichia coli, which was weakly inhibited by rMjALF-D. These data suggest that MjALF-D displays modest antibacterial activity and may provide more insights into the function and role of ALF in shrimp immunity.

Peptide therapeutics in the management of metastatic cancers

Cancer remains a leading health concern threatening lives of millions of patients worldwide. Peptide-based drugs provide a valuable alternative to chemotherapeutics as they are highly specific, cheap, less toxic and easier to synthesize compared to other drugs. In this review, we have discussed various modes in which peptides are being used to curb cancer. Our review highlights specially the various anti-metastatic peptide-based agents developed by targeting a plethora of cellular factors. Herein we have given a special focus on integrins as targets for peptide drugs, as these molecules play key roles in metastatic progression. The review also discusses use of peptides as anti-cancer vaccines and their efficiency as drug-delivery tools. We hope this work will give the reader a clear idea of the mechanisms of peptide-based anti-cancer therapeutics and encourage the development of superior drugs in the future.

The Role of Methyl-(Z)-11-tetradecenoate Acid from the Bacterial Membrane Lipid Composition in Escherichia coli Antibiotic Resistance

Background

The bacterial membrane plays a critical role in the survival of bacteria and the effectiveness of antimicrobial peptides in protecting the host. The lipid constituents of the bacterial membrane are not evenly distributed, and they could be affected by clustering anionic lipids with cationic peptides with multiple positive charges. That could be harmful to bacteria because it prevents lipids from interacting with other molecular components of the cell membrane, disrupts existing natural domains, or creates phase boundary defects between the clustered lipids and the bulk of the membrane. This preliminary quantitative study is aimed at assembling a correlation between antibiotic resistance and bacterial lipid composition in E. coli, based on the function and arrangement of the bilipid coating of the bacterial cell, intimately associated with the path of antimicrobials through membranes.

Methods

Fifteen multiresistant E. coli samples are collected from swine with enterocolitis tested for resistance levels using the disc diffusimetric method (Kirby-Bauer disc diffusion). Pathogen identification completed using the API 20E multitest system revealed the E. coli presence in 11 samples. In these samples, bacterial membrane detection of fatty acid methyl esters (FAME) operating a 240 MS Ion Trap (Varian) GC/MS (Agilent Technologies, Santa Clara, CA, USA) was performed, using the MIDI Sherlock recognition software model.

Results

Interpreting the descriptive statistical method, the correlation matrix, and regression curves and after ANOVA analysis, we ascertained that the studied E. coli population statistically confirmed different degrees of resistance in most of the samples analyzed in this test.

Conclusions

In one case, the methyl-(Z)-11-tetradecenoate acid was observed to have a relationship with the susceptibility evaluation by using the disc diffusimetric method, which has revealed the lowest rate of antimicrobial resistance, so it has importance in further resistance evaluation studies.

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

A review on antimicrobial peptides databases and the computational tools

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Characterization of the Dual Functions of LvCrustinVII from Litopenaeus vannamei as Antimicrobial Peptide and Opsonin

Crustin are a family of antimicrobial peptides that play an important role in protecting against pathogens infection in the innate immune system of crustaceans. Previously, we identified several novel types of crustins, including type VI and type VII crustins. However, their immune functions were still unclear. In the present study, the immune function of type VII crustin LvCrustinVII were investigated in Litopenaeus vannamei. LvCrustinVII was wildly expressed in all tested tissues, with relatively high expression levels in hepatopancreas, epidermis and lymphoid organ. Upon Vibrio parahaemolyticus infection, LvCrustinVII was significantly upregulated in hepatopancreas. Recombinant LvCrustinVII (rLvCrustinVII) showed strong inhibitory activities against Gram-negative bacteria Vibrio harveyi and V. parahaemolyticus, while weak activities against the Gram-positive bacteria Staphylococcus aureus. Binding assay showed that rLvCrustinVII could bind strongly to V. harveyi and V. parahaemolyticus, as well as the cell wall components Glu, LPS and PGN. In the presence of Ca²⁺, rLvCrustinVII could agglutinate V. parahaemolyticus and enhance hemocyte phagocytosis. The present data partially illustrate the immune function of LvCrustinVII, which enrich our understanding on the functional mechanisms of crustins and provide useful information for application of this kind of antimicrobial peptides.

Discovery and Characterization of a New Crustin Antimicrobial Peptide from Amphibalanus amphitrite

Crustins are an antimicrobial peptide (AMP) family that plays an important role in innate immunity in crustaceans. It is important to discover new AMPs from natural sources to expand the current database. Here, we identified and characterized a new crustin family member, named AaCrus1, from Amphibalanus amphitrite. AaCrus1 shares high identity (48.10%) with PvCrus, a Type I crustin of Penaeus vannamei that possesses a whey acidic protein (WAP) domain. AaCrus1 contains 237 amino acids and eight cysteine residues forming conserved 'four-disulfide core' structure. Our recombinant AaCrus1 (rAaCrus 1) could inhibit the growth of two Gram-positive bacteria (Staphylococcus aureus, Bacillus sp. T2) and four Gram-negative bacteria (Vibrio parahaemolyticus, Vibrio harveyi, Vibrio anguillarum, Vibrio alginolyticus) with a minimum inhibitory concentration of 3.5-28 μM. It can further induce agglutination of both Gram-positive and Gram-negative bacteria. rAaCrus1 can bind to bacteria and damage bacterial cell membranes. Furthermore, rAaCrus1 disrupted biofilm development of S. aureus and V. parahaemolyticus. Our discovery and characterization of this new crustin can be further optimized as a good alternative to antibiotics.

Helical structure motifs made searchable for functional peptide design

The systematic design of functional peptides has technological and therapeutic applications. However, there is a need for pattern-based search engines that help locate desired functional motifs in primary sequences regardless of their evolutionary conservation. Existing databases such as The Protein Secondary Structure database (PSS) no longer serves the community, while the Dictionary of Protein Secondary Structure (DSSP) annotates the secondary structures when tertiary structures of proteins are provided. Here, we extract 1.7 million helices from the PDB and compile them into a database (Therapeutic Peptide Design database; TP-DB) that allows queries of compounded patterns to facilitate the identification of sequence motifs of helical structures. We show how TP-DB helps us identify a known purification-tag-specific antibody that can be repurposed into a diagnostic kit for Helicobacter pylori. We also show how the database can be used to design a new antimicrobial peptide that shows better Candida albicans clearance and lower hemolysis than its template homologs. Finally, we demonstrate how TP-DB can suggest point mutations in helical peptide blockers to prevent a targeted tumorigenic protein-protein interaction. TP-DB is made available at http://dyn.life.nthu.edu.tw/design/ .

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types

Predicting antimicrobial peptides (AMPs') function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the 'CNN-BiLSTM-SVM classifier' and 'cellular automata image', a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

BACKGROUND

Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides.

RESULTS

This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2).

CONCLUSIONS

This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides

Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.

Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies

As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets: antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https://github.com/poncey/PreAntiCoV.

Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs.

Macrel: antimicrobial peptide screening in genomes and metagenomes

Motivation

Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall.

Results

Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data.

Availability

Macrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: https://big-data-biology.org/software/macrel.

Antimicrobial and Amyloidogenic Activity of Peptides. Can Antimicrobial Peptides Be Used against SARS-CoV-2?

At present, much attention is paid to the use of antimicrobial peptides (AMPs) of natural and artificial origin to combat pathogens. AMPs have several points that determine their biological activity. We analyzed the structural properties of AMPs, as well as described their mechanism of action and impact on pathogenic bacteria and viruses. Recently published data on the development of new AMP drugs based on a combination of molecular design and genetic engineering approaches are presented. In this article, we have focused on information on the amyloidogenic properties of AMP. This review examines AMP development strategies from the perspective of the current high prevalence of antibiotic-resistant bacteria, and the potential prospects and challenges of using AMPs against infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).