BaAMPs: the database of biofilm-active antimicrobial peptides

Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications

Pathogenic bacteria cause the deaths of millions of people every year. With the development of antibiotics, hundreds and thousands of people's lives have been saved. Nevertheless, bacteria can develop resistance to antibiotics, rendering them insensitive to antibiotics over time. Peptides containing specific amino acids can be used as antibacterial agents; however, they can be easily degraded by proteases in vivo. To address these issues, branched peptide dendrimers are now being considered as good antibacterial agents due to their high efficacy, resistance to protease degradation, and low cytotoxicity. The ease with which peptide dendrimers can be synthesized and modified makes them accessible for use in various biological and nonbiological fields. That is, peptide dendrimers hold a promising future as antibacterial agents with prolonged efficacy without bacterial resistance development. Their in vivo stability and multivalence allow them to effectively target multi-drug-resistant strains and prevent biofilm formation. Thus, it is interesting to have an overview of the development and applications of peptide dendrimers in antibacterial research, including the possibility of employing machine learning approaches for the design of AMPs and dendrimers. This review summarizes the synthesis and applications of peptide dendrimers as antibacterial agents. The challenges and perspectives of using peptide dendrimers as the antibacterial agents are also discussed.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Antimicrobial peptides: An alternative to traditional antibiotics

As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.

A comprehensive guide on screening and selection of a suitable AMP against biofilm-forming bacteria

Lately, antimicrobial resistance (AMR) is increasing at an exponential rate making it important to search alternatives to antibiotics in order to combat multi-drug resistant (MDR) bacterial infections. Out of the several antibacterial and antibiofilm strategies being tested, antimicrobial peptides (AMPs) have shown to give better hopes in terms of a long-lasting solution to the problem. To select a desired AMP, it is important to make right use of available tools and databases that aid in identification, classification, and analysis of the physiochemical properties of AMPs. To identify the targets of these AMPs, it becomes crucial to understand their mode-of-action. AMPs can also be used in combination with other antibacterial and antibiofilm agents so as to achieve enhanced efficacy against bacteria and their biofilms. Due to concerns regarding toxicity, stability, and bioavailability, strategizing drug formulation at an early-stage becomes crucial. Although there are few concerns regarding development of bacterial resistance to AMPs, the evolution of resistance to AMPs occurs extremely slowly. This comprehensive review gives a deep insight into the selection of the right AMP, deciding the right target and combination strategy along with the type of formulation needed, and the possible resistance that bacteria can develop to these AMPs.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/ .

Consolidated knowledge-guided computational pipeline for therapeutic intervention against bacterial biofilms - a review

Biofilm-associated bacterial infections attributed to multifactorial antimicrobial resistance have caused worldwide challenges in formulating successful treatment strategies. In search of accelerated yet cost-effective therapeutics, several researchers have opted for bioinformatics-based protocols to systemize targeted therapies against biofilm-producing strains. The present review investigated the up-to-date computational databases and servers dedicated to anti-biofilm research to design/screen novel biofilm inhibitors (antimicrobial peptides/phytocompounds/synthetic compounds) and predict their biofilm-inhibition efficacy. Scrutinizing the contemporary in silico methods, a consolidated approach has been highlighted, referred to as a knowledge-guided computational pipeline for biofilm-targeted therapy. The proposed pipeline has amalgamated prominently employed methodologies in genomics, transcriptomics, interactomics and proteomics to identify potential target proteins and their complementary anti-biofilm compounds for effective functional inhibition of biofilm-linked pathways. This review can pave the way for new portals to formulate successful therapeutic interventions against biofilm-producing pathogens.

Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides

Introduction

Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends.

Methods

Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs.

Results

EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis.

Conclusion

The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs.

Optimization of antimicrobial peptides for the application against biocorrosive bacteria

Microbiologically influenced corrosion is a common problem in the industrial field due to the deterioration of metals in the presence of various microorganisms, in particular sulfate-reducing bacteria (SRB) and sulfur-oxidizing bacteria (SOB). A common method to reduce microbiologically influenced corrosion is the application of biocides. The limited number of suitable biocides and the resulting development of resistance, high dosage, and high application rate hinder an effective application. An environmentally friendly alternative could be the application of antimicrobial peptides (AMP), which have already been established in the field of medical devices for a while. Here, the successful treatment of different AMPs against 3 SRB and 1 SOB was demonstrated. The peptide L5K5W was favored due to its broad activity, high stability, and simple structure resulting in low synthesis costs. An alanine scan showed that substitution of leucine with tryptophan increased the activity of this peptide twofold compared to the original peptide against D. vulgaris, the main representative of SRB. Additional optimization of this modified peptide through changes in amino acid composition and lipidations significantly increased the effectiveness, finally resulting in a minimum inhibitory concentration (MIC) of 15.63 μg/mL against Desulfovibrio vulgaris. Even against the marine SRB Desulfovibrio indonesiensis with a required salt concentration of min. 2%, an activity of the peptides can be observed (MIC: 31.25 μg/mL). The peptides also remained stable and active for 7 days in the supernatant of the bacterial culture. KEY POINTS: • Antimicrobial peptides provide an alternative to combat biocorrosive bacteria. • Optimization of the peptide sequence leads to a significant increase in activity. • The investigated peptides exhibit high stability, both in the medium and in the bacterial supernatant.

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides

To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections

Background

The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents.

Objectives

This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly.

Methods

A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations.

Results

The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity.

Conclusions

In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.

Peptide Utility (PU) search server: A new tool for peptide sequence search from multiple databases

Proteins are essential building blocks in humans that have garnered huge attention from researchers worldwide due to their numerous therapeutic applications. To date, different computational tools have been developed to extract pre-existing information on these biological molecules, but most of these tools suffer from limitations such as non-user friendly interface, redundancy of data, etc. To overcome these limitations, a user-friendly interface, the Peptide Utility (PU) webserver (https://chain-searching.herokuapp.com/) has been developed for searching and analyzing homologous and identical protein/peptide sequences that can be searched from approximately 0.4 million sequences (structural and sequence information) in both online and offline modes. The PU web server can also be used to study different types of interactions in PDBSum, identifying the most dominating interface residues, the most prevalent interactions, and the interaction preferences of different residues. The webserver would also pave way for the design of novel therapeutic peptides and folds by identifying conserved residues in the three-dimensional structure space of proteins.

CAMPR4: a database of natural and synthetic antimicrobial peptides

There has been an exponential increase in the design of synthetic antimicrobial peptides (AMPs) for its use as novel antibiotics. Synthetic AMPs are substantially enriched in residues with physicochemical properties known to be critical for antimicrobial activity; such as positive charge, hydrophobicity, and higher alpha helical propensity. The current prediction algorithms for AMPs have been developed using AMP sequences from natural sources and hence do not perform well for synthetic peptides. In this version of CAMP database, along with updating sequence information of AMPs, we have created separate prediction algorithms for natural and synthetic AMPs. CAMPR4 holds 24243 AMP sequences, 933 structures, 2143 patents and 263 AMP family signatures. In addition to the data on sequences, source organisms, target organisms, minimum inhibitory and hemolytic concentrations, CAMPR4 provides information on N and C terminal modifications and presence of unusual amino acids, as applicable. The database is integrated with tools for AMP prediction and rational design (natural and synthetic AMPs), sequence (BLAST and clustal omega), structure (VAST) and family analysis (PRATT, ScanProsite, CAMPSign). The data along with the algorithms of CAMPR4 will aid to enhance AMP research. CAMPR4 is accessible at http://camp.bicnirrh.res.in/.

Machine Learning in Antibacterial Drug Design

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

Antioxidant and Antimicrobial Peptides Derived from Food Proteins

Recently, the demand for food proteins in the market has increased due to a rise in degenerative illnesses that are associated with the excessive production of free radicals and the unwanted side effects of various drugs, for which researchers have suggested diets rich in bioactive compounds. Some of the functional compounds present in foods are antioxidant and antimicrobial peptides, which are used to produce foods that promote health and to reduce the consumption of antibiotics. These peptides have been obtained from various sources of proteins, such as foods and agri-food by-products, via enzymatic hydrolysis and microbial fermentation. Peptides with antioxidant properties exert effective metal ion (Fe2+/Cu2+) chelating activity and lipid peroxidation inhibition, which may lead to notably beneficial effects in promoting human health and food processing. Antimicrobial peptides are small oligo-peptides generally containing from 10 to 100 amino acids, with a net positive charge and an amphipathic structure; they are the most important components of the antibacterial defense of organisms at almost all levels of life-bacteria, fungi, plants, amphibians, insects, birds and mammals-and have been suggested as natural compounds that neutralize the toxicity of reactive oxygen species generated by antibiotics and the stress generated by various exogenous sources. This review discusses what antioxidant and antimicrobial peptides are, their source, production, some bioinformatics tools used for their obtainment, emerging technologies, and health benefits.

Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences

A majority of microbial infections are associated with biofilms. Targeting biofilms is considered an effective strategy to limit microbial virulence while minimizing the development of antibiotic resistance. Toward this need, antibiofilm peptides are an attractive arsenal since they are bestowed with properties orthogonal to small molecule drugs. In this work, we developed machine learning models to identify the distinguishing characteristics of known antibiofilm peptides, and to mine peptide databases from diverse habitats to classify new peptides with potential antibiofilm activities. Additionally, we used the reported minimum inhibitory/eradication concentration (MBIC/MBEC) of the antibiofilm peptides to create a regression model on top of the classification model to predict the effectiveness of new antibiofilm peptides. We used a positive dataset containing 242 antibiofilm peptides, and a negative dataset which, unlike previous datasets, contains peptides that are likely to promote biofilm formation. Our model achieved a classification accuracy greater than 98% and harmonic mean of precision-recall (F1) and Matthews correlation coefficient (MCC) scores greater than 0.90; the regression model achieved an MCC score greater than 0.81. We utilized our classification-regression pipeline to evaluate 135,015 peptides from diverse sources for potential antibiofilm activity, and we identified 185 candidates that are likely to be effective against preformed biofilms at micromolar concentrations. Structural analysis of the top 37 hits revealed a larger distribution of helices and coils than sheets, and common functional motifs. Sequence alignment of these hits with known antibiofilm peptides revealed that, while some of the hits showed relatively high sequence similarity with known peptides, some others did not indicate the presence of antibiofilm activity in novel sources or sequences. Further, some of the hits had previously recognized therapeutic properties or host defense traits suggestive of drug repurposing applications. Taken together, this work demonstrates a new in silico approach to predicting antibiofilm efficacy, and identifies promising new candidates for biofilm eradication.

Machine Learning Prediction of Antimicrobial Peptides

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application to Corynebacterium striatum, an Emerging Pathogen

Antimicrobial peptides (AMPs) have been recognized for their ability to target processes important for biofilm formation. Given the vast array of AMPs, identifying potential anti-biofilm candidates remains a significant challenge, and prompts the need for preliminary in silico investigations prior to extensive in vitro and in vivo studies. We have developed Biofilm-AMP (B-AMP), a curated 3D structural and functional repository of AMPs relevant to biofilm studies. In its current version, B-AMP contains predicted 3D structural models of 5544 AMPs (from the DRAMP database) developed using a suite of molecular modeling tools. The repository supports a user-friendly search, using source, name, DRAMP ID, and PepID (unique to B-AMP). Further, AMPs are annotated to existing biofilm literature, consisting of a vast library of over 10,000 articles, enhancing the functional capabilities of B-AMP. To provide an example of the usability of B-AMP, we use the sortase C biofilm target of the emerging pathogen Corynebacterium striatum as a case study. For this, 100 structural AMP models from B-AMP were subject to in silico protein-peptide molecular docking against the catalytic site residues of the C. striatum sortase C protein. Based on docking scores and interacting residues, we suggest a preference scale using which candidate AMPs could be taken up for further in silico, in vitro and in vivo testing. The 3D protein-peptide interaction models and preference scale are available in B-AMP. B-AMP is a comprehensive structural and functional repository of AMPs, and will serve as a starting point for future studies exploring AMPs for biofilm studies. B-AMP is freely available to the community at https://b-amp.karishmakaushiklab.com and will be regularly updated with AMP structures, interaction models with potential biofilm targets, and annotations to biofilm literature.

The Two Weapons against Bacterial Biofilms: Detection and Treatment

Bacterial biofilms are defined as complex aggregates of bacteria that grow attached to surfaces or are associated with interfaces. Bacteria within biofilms are embedded in a self-produced extracellular matrix made of polysaccharides, nucleic acids, and proteins. It is recognized that bacterial biofilms are responsible for the majority of microbial infections that occur in the human body, and that biofilm-related infections are extremely difficult to treat. This is related with the fact that microbial cells in biofilms exhibit increased resistance levels to antibiotics in comparison with planktonic (free-floating) cells. In the last years, the introduction into the market of novel compounds that can overcome the resistance to antimicrobial agents associated with biofilm infection has slowed down. If this situation is not altered, millions of lives are at risk, and this will also strongly affect the world economy. As such, research into the identification and eradication of biofilms is important for the future of human health. In this sense, this article provides an overview of techniques developed to detect and imaging biofilms as well as recent strategies that can be applied to treat biofilms during the several biofilm formation steps.

Current Advances in Lipid and Polymeric Antimicrobial Peptide Delivery Systems and Coatings for the Prevention and Treatment of Bacterial Infections

Bacterial infections constitute a threat to public health as antibiotics are becoming less effective due to the emergence of antimicrobial resistant strains and biofilm and persister formation. Antimicrobial peptides (AMPs) are considered excellent alternatives to antibiotics; however, they suffer from limitations related to their peptidic nature and possible toxicity. The present review critically evaluates the chemical characteristics and antibacterial effects of lipid and polymeric AMP delivery systems and coatings that offer the promise of enhancing the efficacy of AMPs, reducing their limitations and prolonging their half-life. Unfortunately, the antibacterial activities of these systems and coatings have mainly been evaluated in vitro against planktonic bacteria in less biologically relevant conditions, with only some studies focusing on the antibiofilm activities of the formulated AMPs and on the antibacterial effects in animal models. Further improvements of lipid and polymeric AMP delivery systems and coatings may involve the functionalization of these systems to better target the infections and an analysis of the antibacterial activities in biologically relevant environments. Based on the available data we proposed which polymeric AMP delivery system or coatings could be profitable for the treatment of the different hard-to-treat infections, such as bloodstream infections and catheter- or implant-related infections.

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

Efficacy of Indolicidin, Cecropin A (1-7)-Melittin (CAMA) and Their Combination Against Biofilm-Forming Multidrug-Resistant Enteroaggregative Escherichia coli

The present study examined the anti-biofilm efficacy of two short-chain antimicrobial peptides (AMPs), namely, indolicidin and cecropin A (1-7)-melittin (CAMA) against biofilm-forming multidrug-resistant enteroaggregative Escherichia coli (MDR-EAEC) isolates. The typical EAEC isolates re-validated by PCR and confirmed using HEp-2 cell adherence assay was subjected to antibiotic susceptibility testing to confirm its MDR status. The biofilm-forming ability of MDR-EAEC isolates was assessed by Congo red binding, microtitre plate assays and hydrophobicity index; broth microdilution technique was employed to determine minimum inhibitory concentrations (MICs) and minimum biofilm eradication concentrations (MBECs). The obtained MIC and MBEC values for both AMPs were evaluated alone and in combination against MDR-EAEC biofilms using crystal violet (CV) staining and confocal microscopy-based live/dead cell quantification methods. All the three MDR-EAEC strains revealed weak to strong biofilm-forming ability and were found to be electron-donating and weakly electron-accepting (hydrophobicity index). Also, highly significant (P < 0.001) time-dependent hydrodynamic growth of the three MDR-EAEC strains was observed at 48 h of incubation in Dulbecco's modified Eagle's medium (DMEM) containing 0.45% D-glucose. AMPs and their combination were able to inhibit the initial biofilm formation at 24 h and 48 h as evidenced by CV staining and confocal quantification. Further, the application of AMPs (individually and combination) against the preformed MDR-EAEC biofilms resulted in highly significant eradication (P < 0.001) at 24 h post treatment. However, significant differences were not observed between AMP treatments (individually or in combination). The AMPs seem to be an effective candidates for further investigations such as safety, stability and appropriate biofilm-forming MDR-EAEC animal models.

Unveiling Putative Functions of Mucus Proteins and Their Tryptic Peptides in Seven Gastropod Species Using Comparative Proteomics and Machine Learning-Based Bioinformatics Predictions

Gastropods are among the most diverse animals. Gastropod mucus contains several glycoproteins and peptides that vary by species and habitat. Some bioactive peptides from gastropod mucus were identified only in a few species. Therefore, using biochemical, mass spectrometric, and bioinformatics approaches, this study aimed to comprehensively identify putative bioactive peptides from the mucus proteomes of seven commonly found or commercially valuable gastropods. The mucus was collected in triplicate samples, and the proteins were separated by 1D-SDS-PAGE before tryptic digestion and peptide identification by nano LC-MS/MS. The mucus peptides were subsequently compared with R scripts. A total of 2818 different peptides constituting 1634 proteins from the mucus samples were identified, and 1218 of these peptides (43%) were core peptides found in the mucus of all examined species. Clustering and correspondence analyses of 1600 variable peptides showed unique mucous peptide patterns for each species. The high-throughput k-nearest neighbor and random forest-based prediction programs were developed with more than 95% averaged accuracy and could identify 11 functional categories of putative bioactive peptides and 268 peptides (9.5%) with at least five to seven bioactive properties. Antihypertensive, drug-delivering, and antiparasitic peptides were predominant. These peptides provide an understanding of gastropod mucus, and the putative bioactive peptides are expected to be experimentally validated for further medical, pharmaceutical, and cosmetic applications.

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set

In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.

Insect antimicrobial peptides: potential weapons to counteract the antibiotic resistance

Misuse and overuse of antibiotics have contributed in the last decades to a phenomenon known as antibiotic resistance which is currently considered one of the principal threats to global public health by the World Health Organization. The aim to find alternative drugs has been demonstrated as a real challenge. Thanks to their biodiversity, insects represent the largest class of organisms in the animal kingdom. The humoral immune response includes the production of antimicrobial peptides (AMPs) that are released into the insect hemolymph after microbial infection. In this review, we have focused on insect immune responses, particularly on AMP characteristics, their mechanism of action and applications, especially in the biomedical field. Furthermore, we discuss the Toll, Imd, and JAK-STAT pathways that activate genes encoding for the expression of AMPs. Moreover, we focused on strategies to improve insect peptides stability against proteolytic susceptibility such as D-amino acid substitutions, N-terminus modification, cyclization and dimerization.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance

Inappropriate and injudicious use of antimicrobial drugs in human health, hygiene, agriculture, animal husbandry and food industries has contributed significantly to rapid emergence and persistence of antimicrobial resistance (AMR), one of the serious global public health threats. The crisis of AMR versus slower discovery of newer antibiotics put forth a daunting task to control these drug-resistant superbugs. Several phyto-antimicrobials have been identified in recent years with direct-killing (bactericidal) and/or drug-resistance reversal (re-sensitization of AMR phenotypes) potencies. Phyto-antimicrobials may hold the key in combating AMR owing to their abilities to target major microbial drug-resistance determinants including cell membrane, drug-efflux pumps, cell communication and biofilms. However, limited distribution, low intracellular concentrations, eco-geographical variations, beside other considerations like dynamic environments, climate change and over-exploitation of plant-resources are major blockades in full potential exploration phyto-antimicrobials. Synthetic biology (SynBio) strategies integrating metabolic engineering, RNA-interference, genome editing/engineering and/or systems biology approaches using plant chassis (as engineerable platforms) offer prospective tools for production of phyto-antimicrobials. With expanding SynBio toolkit, successful attempts towards introduction of entire gene cluster, reconstituting the metabolic pathway or transferring an entire metabolic (or synthetic) pathway into heterologous plant systems highlight the potential of this field. Through this perspective review, we are presenting herein the current situation and options for addressing AMR, emphasizing on the significance of phyto-antimicrobials in this apparently post-antibiotic era, and effective use of plant chassis for phyto-antimicrobial production at industrial scales along with major SynBio tools and useful databases. Current knowledge, recent success stories, associated challenges and prospects of translational success are also discussed.

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and, via engineering, can be reconfigured for optimized potency and targetability toward drug-resistant pathogens. Here, we review the recent development of tools for the discovery, design, and production of AMPs and propose that the future of peptide drug discovery will involve the convergence of computational and synthetic biology principles.

Exploring Galleria mellonella larval model to evaluate antibacterial efficacy of Cecropin A (1-7)-Melittin against multi-drug resistant enteroaggregative Escherichia coli

High throughput in vivo laboratory models is need for screening and identification of effective therapeutic agents to overcome microbial drug-resistance. This study was undertaken to evaluate in vivo antimicrobial efficacy of short-chain antimicrobial peptide- Cecropin A (1-7)-Melittin (CAMA) against three multi-drug resistant enteroaggregative Escherichia coli (MDR-EAEC) field isolates in a Galleria mellonella larval model. The minimum inhibitory concentration (MIC; 2.0 mg/L) and minimum bactericidal concentration (MBC; 4.0 mg/L) of CAMA were determined by microdilution assay. CAMA was found to be stable at high temperatures, physiological concentration of cationic salts and proteases; safe with sheep erythrocytes, secondary cell lines and commensal lactobacilli at lower MICs; and exhibited membrane permeabilization. In vitro time-kill assay revealed concentration- and time-dependent clearance of MDR-EAEC in CAMA-treated groups at 30 min. CAMA- treated G. mellonella larvae exhibited an increased survival rate, reduced MDR-EAEC counts, immunomodulatory effect and proved non-toxic which concurred with histopathological findings. CAMA exhibited either an equal or better efficacy than the tested antibiotic control, meropenem. This study highlights the possibility of G. mellonella larvae as an excellent in vivo model for investigating the host-pathogen interaction, including the efficacy of antimicrobials against MDR-EAEC strains.

Therapeutic Potential of Antimicrobial Peptides in Polymicrobial Biofilm-Associated Infections

It is widely recognized that many chronic infections of the human body have a polymicrobial etiology. These include diabetic foot ulcer infections, lung infections in cystic fibrosis patients, periodontitis, otitis, urinary tract infections and even a proportion of systemic infections. The treatment of mixed infections poses serious challenges in the clinic. First, polymicrobial communities of microorganisms often organize themselves as biofilms that are notoriously recalcitrant to antimicrobial therapy and clearance by the host immune system. Secondly, a plethora of interactions among community members may affect the expression of virulence factors and the susceptibility to antimicrobials of individual species in the community. Therefore, new strategies able to target multiple pathogens in mixed populations need to be urgently developed and evaluated. In this regard, antimicrobial or host defense peptides (AMPs) deserve particular attention as they are endowed with many favorable features that may serve to this end. The aim of the present review is to offer a comprehensive and updated overview of studies addressing the therapeutic potential of AMPs in mixed infections, highlighting the opportunities offered by this class of antimicrobials in the fight against polymicrobial infections, but also the limits that may arise in their use for this type of application.

Databases for the study of biofilms: current status and potential applications

Biofilms play an important role in health, being associated with >80% of all microbial infections in the body and in the development of antibiotic resistance. Research in this field has continuously produced large volumes of data. Being able to handle all this information will be paramount for progress in this field. However, this places a heavy burden on the development of strategies to gather, organize and make this information available in a way that can be readily and effectively used by those requiring it. Lately, efforts towards this goal have been reported, particularly with the development of Quorumpeps, BiofOmics, BaAMPs, QSPpred, dPABBs, aBiofilm and the Biofilms Structural Database. This work reviews these databases and highlights their applicability and potential, while stressing some of the challenges for the coming years in database development and usage brought about by the use of big data and machine learning.

Unraveling dominant surface physicochemistry to build antimicrobial peptide coatings with supramolecular amphiphiles

With the increasing threat from antibiotic-resistant bacteria, surface modification with antimicrobial peptides (AMP) has been promisingly explored for preventing bacterial infections. Little is known about the critical factors that govern AMP-surface interactions to obtain stable and active coatings. Here, we systematically monitored the adsorption of a designer amphipathic AMP, GL13K, on model surfaces. Self-assembly of the GL13K peptides formed supramolecular amphiphiles that highly adsorbed on negatively charged, polar hydroxyapatite-coated sensors. We further tuned surface charge and/or surface polarity with self-assembled monolayers (SAMs) on Au sensors and studied their interactions with adsorbed GL13K. We determined that the surface polarity of the SAM-coated sensors instead of their surface charge was the dominant factor governing AMP/substrate interactions via hydrogen bonding. Our findings will instruct the universal design of efficient self-assembled AMP coatings on biomaterials, biomedical devices and/or natural tissues.

Exploiting Lactoferricin (17-30) as a Potential Antimicrobial and Antibiofilm Candidate Against Multi-Drug-Resistant Enteroaggregative Escherichia coli

The study evaluated the in vitro antimicrobial and antibiofilm efficacy of an antimicrobial peptide (AMP), lactoferricin (17–30) [Lfcin (17–30)], against biofilm-forming multi-drug-resistant (MDR) strains of enteroaggregative Escherichia coli (EAEC), and subsequently, the in vivo antimicrobial efficacy was assessed in a Galleria mellonella larval model. Initially, minimum inhibitory concentration (MIC; 32 μM), minimum bactericidal concentration (MBC; 32 μM), and minimum biofilm eradication concentration (MBEC; 32 μM) of Lfcin (17–30) were determined against MDR-EAEC field isolates (n = 3). Lfcin (17–30) was tested stable against high-end temperatures (70 and 90°C), physiological concentration of cationic salts (150 mM NaCl and 2 mM MgCl₂), and proteases (proteinase-K and lysozyme). Further, at lower MIC, Lfcin (17–30) proved to be safe for sheep RBCs, secondary cell lines (HEp-2 and RAW 264.7), and beneficial gut lactobacilli. In the in vitro time-kill assay, Lfcin (17–30) inhibited the MDR-EAEC strains 3 h post-incubation, and the antibacterial effect was due to membrane permeation of Lfcin (17–30) in the inner and outer membranes of MDR-EAEC. Furthermore, in the in vivo experiments, G. mellonella larvae treated with Lfcin (17–30) exhibited an increased survival rate, lower MDR-EAEC counts (P < 0.001), mild to moderate histopathological changes, and enhanced immunomodulatory effect and were safe to larval cells when compared with infection control. Besides, Lfcin (17–30) proved to be an effective antibiofilm agent, as it inhibited and eradicated the preformed biofilm formed by MDR-EAEC strains in a significant (P < 0.05) manner both by microtiter plate assay and live/dead bacterial quantification-based confocal microscopy. We recommend further investigation of Lfcin (17–30) in an appropriate animal model before its application in target host against MDR-EAEC strains.

Antimicrobial efficacy of Cecropin A (1-7)- Melittin and Lactoferricin (17-30) against multi-drug resistant Salmonella Enteritidis

The present study evaluated intracellular antibacterial efficacy of two short-chain cationic antimicrobial peptides (AMPs) namely, Cecropin A (1-7)-Melittin and lactoferricin (17-30) against three field strains of multi-drug resistant Salmonella Enteritidis. Initially, antimicrobial ability of both the AMPs was evaluated for their minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) against multi-drug resistant S. Enteritidis strains. Besides, the AMPs were evaluated for its in vitro stability (high-end temperatures, proteases, physiological concentrations of cationic salts and pH) and safety (haemolytic assay in sheep erythrocytes; cytotoxicity assay in murine macrophage RAW 264.7 cell line and human epithelioma HEp-2 cell line and beneficial gut lactobacilli). Later, a time-kill assay was performed to assess the intracellular antibacterial activity of Cecropin A (1-7)-Melittin and lactoferricin (17-30) against multi-drug resistant S. Enteritidis in RAW 264.7 and HEp-2 cells. The observed MBC values of Cecropin A (1-7)-Melittin and lactoferricin (17-30) against multi-drug resistant S. Enteritidis (128 μM; 256 μM) were generally twice or four-fold greater than the MIC values (64 μM). Further, both the AMPs were found variably stable after exposure at high-end temperatures (70 °C and 90 °C), protease treatment (trypsin, proteinase K, lysozyme), higher concentration of physiological salts (150 mM NaCl and 2 mM MgCl₂) and hydrogen ion concentrations (pH 4.0 to 8.0). Both the AMPs were found non-haemolytic on sheep erythrocytes, revealed minimal cytotoxicity in RAW 264.7 and HEp-2 cells, and was tested safe against beneficial gut lactobacilli (L. acidophilus and L. rhamnosus). Intracellular bacteriostatic effect with both cationic AMPs against multi-drug resistant S. Enteritidis was evident in RAW 264.7 cells; however, in both the cell lines, the significant bactericidal effect was not observed (P > 0.05) with both cationic AMPs understudy against multi-drug resistant S. Enteritidis. Based on the results of the present study, both the cationic AMPs understudy may not be useful for the intracellular elimination of multi-drug resistant S. Enteritidis; hence, further studies such as conjugation of these AMPs with either cell-penetrating peptides (CPP) and/or nanoparticles (NPs) are warranted.

Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis

MOTIVATION

Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites.

RESULTS

After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options.

AVAILABILITY AND IMPLEMENTATION

Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Setting New Routes for Antifungal Drug Discovery Against Pathogenic Fungi

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Fungal diseases are life-threatening to human health and responsible for millions of deaths around the world. Fungal pathogens lead to a high number of morbidity and mortality. Current antifungal treatment comprises drugs, such as azoles, echinocandins, and polyenes and the cure is not guaranteed. In addition, such drugs are related to severe side effects and the treatment lasts for an extended period. Thus, setting new routes for the discovery of effective and safe antifungal drugs should be a priority within the health care system. The discovery of alternative and efficient antifungal drugs showing fewer side effects is time-consuming and remains a challenge. Natural products can be a source of antifungals and used in combinatorial therapy. The most important natural products are antifungal peptides, antifungal lectins, antifungal plants, and fungi secondary metabolites. Several proteins, enzymes, and metabolic pathways could be targets for the discovery of efficient inhibitor compounds and recently, heat shock proteins, calcineurin, salinomycin, the trehalose biosynthetic pathway, and the glyoxylate cycle have been investigated in several fungal species. HSP protein inhibitors and echinocandins have been shown to have a fungicidal effect against azole-resistant fungi strains. Transcriptomic and proteomic approaches have advanced antifungal drug discovery and pointed to new important specific-pathogen targets. Certain enzymes, such as those from the glyoxylate cycle, have been a target of antifungal compounds in several fungi species. Natural and synthetic compounds inhibited the activity of such enzymes and reduced the ability of fungal cells to transit from mycelium to yeast, proving to be promisor antifungal agents. Finally, computational biology has developed effective approaches, setting new routes for early antifungal drug discovery since normal approaches take several years from discovery to clinical use. Thus, the development of new antifungal strategies might reduce the therapeutic time and increase the quality of life of patients.

Somuncurins: Bioactive Peptides from the Skin of the Endangered Endemic Patagonian Frog Pleurodema somuncurense

The skin glands of amphibian species hold a major component of their innate immunity, namely a unique set of antimicrobial peptides (AMPs). Although most of them have common characteristics, differences in AMP sequences allow a huge repertoire of biological activity with varying degrees of efficacy. We present the first study of the AMPs from Pleurodema somuncurence (Anura: Leptodactylidae: Leiuperinae). Among the 11 identified mature peptides, three presented antimicrobial activity. Somuncurin-1 (FIIWPLRYRK), somuncurin-2 (FILKRSYPQYY), and thaulin-3 (NLVGSLLGGILKK) inhibited Escherichia coli growth. Somuncurin-1 also showed antimicrobial activity against Staphylococcus aureus. Biophysical membrane model studies revealed that this peptide had a greater permeation effect in prokaryotic-like membranes and capacity to restructure liposomes, suggesting fusogenic activity, which could lead to cell aggregation and disruption of cell morphology. This study contributes to the characterization of peptides with new sequences to enrich the databases for the design of therapeutic agents. Furthermore, it highlights the importance of investing in nature conservation and the power of genetic description as a strategy to identify new compounds.

BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors

Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.

PlantPepDB: A manually curated plant peptide database

Plants produce an array of peptides as part of their innate defense mechanism against pathogens. The potential use of these peptides for various therapeutic purposes is increasing per diem. In order to excel in this research, the community requires web repositories that provide reliable and accurate information about these phyto-peptides. This work is an attempt to bridge the gaps in plant-based peptide research. PlantPepDB is a manually curated database that consists of 3848 plant-derived peptides among which 2821 are experimentally validated at the protein level, 458 have experimental evidence at the transcript level, 530 are predicted and only 39 peptides are inferred from homology. Incorporation of physicochemical properties and tertiary structure into PlantPepDB will help the users to study the therapeutic potential of a peptide, thus, debuts as a powerful resource for therapeutic research. Different options like Simple, Advanced, PhysicoChem and AA composition search along with browsing utilities are provided in the database for the users to execute dynamic search and retrieve the desired data. Interestingly, many peptides that were considered to possess only a single property were found to exhibit multiple properties after careful curation and merging the duplicate data that was collected from published literature and already available databases. Overall, PlantPepDB is the first database comprising detailed analysis and comprehensive information of phyto-peptides from a broad functional range which will be useful for peptide-based applied research. PlantPepDB is freely available at http://www.nipgr.ac.in/PlantPepDB/.

Antimicrobial Efficacy of Indolicidin Against Multi-Drug Resistant Enteroaggregative Escherichia coli in a Galleria mellonella Model

Antimicrobial resistance against enteroaggregative Escherichia coli (EAEC), an emerging food-borne pathogen, has been observed in an increasing trend recently. In the recent wake of antimicrobial resistance, alternate strategies especially, cationic antimicrobial peptides (AMPs) have attracted considerable attention to source antimicrobial technology solutions. This study evaluated the in vitro antimicrobial efficacy of Indolicidin against multi-drug resistant enteroaggregative Escherichia coli (MDR-EAEC) strains and further to assess its in vivo antimicrobial efficacy in Galleria mellonella larval model. The minimum inhibitory concentration (MIC; 32 μM) and minimum bactericidal concentration (MBC; 64 μM) of Indolicidin against MDR-EAEC was determined by micro broth dilution method. Indolicidin was also tested for its stability (high-end temperatures, physiological concentration of salts and proteases); safety (sheep RBCs; HEp-2 and RAW 264.7 cell lines); effect on beneficial microflora (Lactobacillus rhamnosus and Lactobacillus acidophilus) and its mode of action (flow cytometry; nitrocefin and ONPG uptake). In vitro time-kill kinetic assay of MDR-EAEC treated with Indolicidin was performed. Further, survival rate, MDR-EAEC count, melanization rate, hemocyte enumeration, cytotoxicity assay and histopathological examination were carried out in G. mellonella model to assess in vivo antimicrobial efficacy of Indolicidin against MDR-EAEC strains. Indolicidin was tested stable at high temperatures (70°C; 90°C), physiological concentration of cationic salts (NaCl; MgCl₂) and proteases, except for trypsin and tested safe with sheep RBCs and cell lines (RAW 264.7; HEp-2) at MIC (1X and 2X); the beneficial flora was not inhibited. Indolicidin exhibited outer membrane permeabilization in a concentration- and time-dependent manner. In vitro time-kill assay revealed concentration-cum-time dependent clearance of MDR-EAEC in Indolicidin-treated groups at 120 min, while, in G. mellonella, the infected group treated with Indolicidin revealed an increased survival rate, immunomodulatory effect, reduced MDR-EAEC counts and were tested safe to the larval cells which was concurred histopathologically. To conclude, the results suggests Indolicidin as an effective antimicrobial candidate against MDR-EAEC and we recommend its further investigation in appropriate animal models (mice/piglets) before its application in the target host.

ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user's research

ADAPTABLE is a webserver and database of antimicrobial peptides that uses sequence and property alignment to highlight their mode of action against the threat of resistance in medicine and agriculture.

Antimicrobial peptides (AMPs) are part of the innate immune response to pathogens in all of the kingdoms of life. They have received significant attention because of their extraordinary variety of activities, in particular, as candidate drugs against the threat of super-bacteria. A systematic study of the relation between the sequence and the mechanism of action is urgently needed, given the thousands of sequences already in multiple web resources. ADAPTABLE web platform (http://gec.u-picardie.fr/adaptable) introduces the concept of “property alignment” to create families of property and sequence-related peptides (SR families). This feature provides the researcher with a tool to select those AMPs meaningful to their research from among more than 40,000 nonredundant sequences. Selectable properties include the target organism and experimental activity concentration, allowing selection of peptides with multiple simultaneous actions. This is made possible by ADAPTABLE because it not only merges sequences of AMP databases but also merges their data, thereby standardizing values and handling non-proteinogenic amino acids. In this unified platform, SR families allow the creation of peptide scaffolds based on common traits in peptides with similar activity, independently of their source.

Peptide Mix from Olivancillaria hiatula Interferes with Cell-to-Cell Communication in Pseudomonas aeruginosa

Bacteria in biofilms are encased in an extracellular polymeric matrix that limits exposure of microbial cells to lethal doses of antimicrobial agents, leading to resistance. In Pseudomonas aeruginosa, biofilm formation is regulated by cell-to-cell communication, called quorum sensing. Quorum sensing facilitates a variety of bacterial physiological functions such as swarming motility and protease, pyoverdine, and pyocyanin productions. Peptide mix from the marine mollusc, Olivancillaria hiatula, has been studied for its antibiofilm activity against Pseudomonas aeruginosa. Microscopy and microtiter plate-based assays were used to evaluate biofilm inhibitory activities. Effect of the peptide mix on quorum sensing-mediated processes was also evaluated. Peptide mix proved to be a good antibiofilm agent, requiring less than 39 μg/mL to inhibit 50% biofilm formation. Micrographs obtained confirmed biofilm inhibition at 1/2 MIC whereas 2.5 mg/mL was required to degrade preformed biofilm. There was a marked attenuation in quorum sensing-mediated phenotypes as well. At 1/2 MIC of peptide, the expression of pyocyanin, pyoverdine, and protease was inhibited by 60%, 72%, and 54%, respectively. Additionally, swarming motility was repressed by peptide in a dose-dependent manner. These results suggest that the peptide mix from Olivancillaria hiatula probably inhibits biofilm formation by interfering with cell-to-cell communication in Pseudomonas aeruginosa.

Natural Occurrence in Venomous Arthropods of Antimicrobial Peptides Active against Protozoan Parasites

Arthropoda is a phylum of invertebrates that has undergone remarkable evolutionary radiation, with a wide range of venomous animals. Arthropod venom is a complex mixture of molecules and a source of new compounds, including antimicrobial peptides (AMPs). Most AMPs affect membrane integrity and produce lethal pores in microorganisms, including protozoan pathogens, whereas others act on internal targets or by modulation of the host immune system. Protozoan parasites cause some serious life-threatening diseases among millions of people worldwide, mostly affecting the poorest in developing tropical regions. Humans can be infected with protozoan parasites belonging to the genera Trypanosoma, Leishmania, Plasmodium, and Toxoplasma, responsible for Chagas disease, human African trypanosomiasis, leishmaniasis, malaria, and toxoplasmosis. There is not yet any cure or vaccine for these illnesses, and the current antiprotozoal chemotherapeutic compounds are inefficient and toxic and have been in clinical use for decades, which increases drug resistance. In this review, we will present an overview of AMPs, the diverse modes of action of AMPs on protozoan targets, and the prospection of novel AMPs isolated from venomous arthropods with the potential to become novel clinical agents to treat protozoan-borne diseases.