Computational tools for exploring sequence databases as a resource for antimicrobial peptides

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.

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

Complete Genome Sequencing and Bacteriocin Functional Characterization of Pediococcus ethanolidurans CP201 from Daqu

This study aims to sequence the whole genome of Pediococcus ethanolidurans CP201 isolated from Daqu and determine the anti-corrosion ability of bacteriocins on chicken breast. The whole genome sequence information of P. ethanolidurans CP201 was analyzed, and its gene structure and function were explored. It was found that gene1164 had annotations in the NR, Pfam, and Swiss-Prot databases, and was related to bacteriocins. The exogenous expression of the bacteriocin gene Pediocin PE-201 was analyzed based on the pET-21b vector and the host BL21, and the corresponding bacteriocin was successfully expressed under the induction of IPTG. After purification by NI-NTA column, enterokinase treatment, membrane dialysis concentration treatment, and SDS-PAGE electrophoresis, the molecular weight was about 6.5 kDa and the purity was above 90%. By applying different concentrations of bacteriocin to chicken breast with different levels of contamination, the control of pathogenic bacteria, the ordinary contamination level (OC) group, and the high contamination level (MC) group could be completely achieved with 25 mg/L bacteriocin. In conclusion, the bacteriocin produced by the newly isolated CP201 can be applied to the preservation of meat products to prevent the risk of food-borne diseases.

Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening

With the accelerating growth of antimicrobial resistance (AMR), there is an urgent need for new antimicrobial agents with low or no AMR. Antimicrobial peptides (AMPs) have been extensively studied as alternatives to antibiotics (ATAs). Coupled with the new generation of high-throughput technology for AMP mining, the number of derivatives has increased dramatically, but manual running is time-consuming and laborious. Therefore, it is necessary to establish databases that combine computer algorithms to summarize, analyze, and design new AMPs. A number of AMP databases have already been established, such as the Antimicrobial Peptides Database (APD), the Collection of Antimicrobial Peptides (CAMP), the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and the Database of Antimicrobial Peptides (dbAMPs). These four AMP databases are comprehensive and are widely used. This review aims to cover the construction, evolution, characteristic function, prediction, and design of these four AMP databases. It also offers ideas for the improvement and application of these databases based on merging the various advantages of these four peptide libraries. This review promotes research and development into new AMPs and lays their foundation in the fields of druggability and clinical precision treatment.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 10⁶⁵ unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Novel antimicrobial agents targeting the Streptococcus mutans biofilms discovery through computer technology

Dental caries is one of the most prevalent and costly biofilm-associated infectious diseases worldwide. Streptococcus mutans (S. mutans) is well recognized as the major causative factor of dental caries due to its acidogenicity, aciduricity and extracellular polymeric substances (EPSs) synthesis ability. The EPSs have been considered as a virulent factor of cariogenic biofilm, which enhance biofilms resistance to antimicrobial agents and virulence compared with planktonic bacterial cells. The traditional anti-caries therapies, such as chlorhexidine and antibiotics are characterized by side-effects and drug resistance. With the development of computer technology, several novel approaches are being used to synthesize or discover antimicrobial agents. In this mini review, we summarized the novel antimicrobial agents targeting the S. mutans biofilms discovery through computer technology. Drug repurposing of small molecules expands the original medical indications and lowers drug development costs and risks. The computer-aided drug design (CADD) has been used for identifying compounds with optimal interactions with the target via silico screening and computational methods. The synthetic antimicrobial peptides (AMPs) based on the rational design, computational design or high-throughput screening have shown increased selectivity for both single- and multi-species biofilms. These methods provide potential therapeutic agents to promote targeted control of the oral microbial biofilms in the near future.

Tools and techniques for rational designing of antimicrobial peptides for aquaculture

Fisheries and aquaculture industries remain essential sources of food and nutrition for millions of people worldwide. Indiscriminate use of antibiotics has led to the emergence of antimicrobial-resistant bacteria and posed a severe threat to public health. Researchers have opined that antimicrobial peptides (AMPs) can be the best possible alternative to curb the rising tide of antimicrobial resistance in aquaculture. AMPs may also help to achieve the objectives of one health approach. The natural AMPs are associated with several shortcomings, like less in vivo stability, toxicity to host cell, high cost of production and low potency in a biological system. In this review, we have provided a comprehensive outline about the strategies for designing synthetic mimics of natural AMPs with high potency. Moreover, the freely available AMP databases and the information about the molecular docking tools are enlisted. We also provided in silico template for rationally designing the AMPs from fish piscidins or other peptides. The rationally designed piscidin (rP1 and rp2) may be used to tackle microbial infections in aquaculture. Further, the protocol can be used to develop the truncated mimics of natural AMPs having more potency and protease stability.

Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage

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

Emerging Computational Approaches for Antimicrobial Peptide Discovery

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

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.

Screening for cysteine-stabilized scaffolds for developing proteolytic-resistant AMPs

Antimicrobial peptides (AMP) are present in all organisms and can present several activities and potential applications in human and animal health. Screening these molecules scaffolds represents a key point for discovering and developing novel biotechnological products, including antimicrobial, antiviral and anticancer drugs candidates and insecticidal molecules with potential applications in agriculture. Therefore, considering the amount of biological data currently deposited on public databases, computational approaches have been commonly used to predicted and identify novel cysteine-rich peptides scaffolds with known or unknown biological properties. Here, we describe a step-by-step in silico screening for cysteine-rich peptides employing molecular modeling (with a core focus on comparative modeling) and atomistic molecular dynamics simulations. Moreover, we also present the concept of additional tools aiming at the computer-aided screening of new Cs-AMPs based drug candidates. After the computational screening and peptide chemical synthesis, we also provide the reader with a step-by-step in vitro activity evaluation of these candidates, including antibacterial, antifungal, and antiviral assays.

Recent advances in the design of antimicrobial peptide conjugates

Antimicrobial peptides (AMPs) are ubiquitous host defense peptides characterized by antibiotic activity and lower propensity for developing resistance compared to classic antibiotics. While several AMPs have shown activity against antibiotic-sensitive...

An overview of databases and bioinformatics tools for plant antimicrobial peptides

Antimicrobial peptides (AMPs) are small, ribosomally synthesized proteins found in nearly all forms of life. In plants, AMPs play a central role in plant defense due to their distinct physicochemical properties. Due to their broad-spectrum antimicrobial activity and rapid killing action, plant AMPs have become important candidates for the development of new drugs to control plant and animal pathogens that are resistant to multiple drugs. Further research is required to explore the potential uses of these natural compounds. Computational strategies have been increasingly used to understand key aspects of antimicrobial peptides. These strategies will help to minimize the time and cost of "wet-lab" experimentation. Researchers have developed various tools and databases to provide updated information on AMPs. However, despite the increased availability of antimicrobial peptide resources in biological databases, finding AMPs from plants can still be a difficult task. The number of plant AMP sequences in current databases is still small and yet often redundant. To facilitate further characterization of plant AMPs, we have summarized information on the location, distribution, and annotations of plant AMPs available in the most relevant databases for AMPs research. We also mapped and categorized the bioinformatics tools available in these databases. We expect that this will allow researchers to advance in the discovery and development of new plant AMPs with potent biological properties. We hope to provide insights to further expand the application of AMPs in the fields of biotechnology, pharmacy, and agriculture.

Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment

BACKGROUND

Computer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present "Sense the Moment", a prediction system capable of discriminating AMPs and shuffled versions.

METHODS

The system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales.

RESULTS

The model showed good accuracy (>80%) and excellent sensitivity (>90%) for AMP prediction, exceeding deep-learning-based methods.

CONCLUSION

Our results demonstrate the system's applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives.

GENERAL SIGNIFICANCE

The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis.

AVAILABILITY

The system was implemented as a web application, available at <http://portoreports.com/stm/>.

Bioinformatic analysis and antiviral effect of Periplaneta americana defensins

Due to the lack of an adaptive immune system, insects rely on innate immune mechanisms to fight against pathogenic infections. Two major innate immune pathways, Toll and IMD, orchestrate anti-pathogen responses by regulating the expression of antimicrobial peptide (AMP) genes. Although the antifungal or antibacterial function of AMPs has been well characterized, the antiviral role of AMPs in insects remains largely unclear. Periplaneta americana (P. americana), or the American cockroach, is used in traditional Chinese medicine as an antiviral agent; however, the underlying mechanism of action of P. americana extracts is unclear. Our previous study showed that the P. americana genome encodes multiple antimicrobial peptide genes. Based on these data, we predicted five novel P. americana defensins (PaDefensins) and analyzed their primary structure, secondary structure, and physicochemical properties. The putative antiviral, antifungal, antibacterial, and anticancer activities suggested that PaDefensin5 is a desirable therapeutic candidate against viral diseases. As the first experimental evidence of the antiviral effects of insect defensins, we also showed the antiviral effect of PaDefensin5 in Drosophila Kc cells and Drosophila embryos in vivo . In conclusion, results of both in silico predictions and subsequent antiviral experiments suggested PaDefensin5 a promising antiviral drug.

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.

Building HMM and molecular docking analysis for the sensitive detection of anti-viral pneumonia antimicrobial peptides (AMPs)

Pneumonia is the main reason for mortality among children under five years, causing 1.6 million deaths every year; late research has exhibited that mortality is increasing in the elderly. A few biomarkers used for its diagnosis need specificity and precision, as they are related to different infections, for example, pulmonary tuberculosis and Human Immunodeficiency Virus. There is a quest for new biomarkers worldwide to diagnose the disease to defeat these previously mentioned constraints. Antimicrobial peptides (AMPs) are promising indicative specialists against infection. This research work used AMPs as biomarkers to detect viral pneumonia pathogens, for example, Respiratory syncytial virus, Influenza A and B viruses utilizing in silico technologies, such as Hidden Markov Model (HMMER). HMMER was used to distinguish putative anti-viral pneumonia AMPs against the recognized receptor proteins of Respiratory syncytial virus, Influenza A, and B viruses. The physicochemical parameters of these putative AMPs were analyzed, and their 3-D structures were determined utilizing I-TASSER. Molecular docking interaction of these AMPs against the recognized viral pneumonia proteins was carried out using the PATCHDOCK and HDock servers. The results demonstrated 27 anti-viral AMPs ranked based on their E values with significant physicochemical parameters in similarity with known experimentally approved AMPs. The AMPs additionally had a high anticipated binding potential to the pneumonia receptors of these microorganisms sensitively. The tendency of the putative anti-viral AMPs to bind pneumonia proteins showed that they would be promising applicant biomarkers to identify these viral microorganisms in the point-of-care (POC) pneumonia diagnostics. The high precision observed for the AMPs legitimizes HMM's utilization in the disease diagnostics' discovery process.

Strategies toward development of antimicrobial biomaterials for dental healthcare applications

Several approaches for elimination of oral pathogens are being explored at the present time since oral diseases remain prevalent affecting approximately 3.5 billion people worldwide. Need for antimicrobial biomaterials in dental healthcare include but is not restricted to designing resin composites and adhesives for prevention of dental caries. Constant efforts are also being made to develop antimicrobial strategies for clearance of endodontic space prior root canal treatment and for treatment of periimplantitis and periodontitis. This article discusses various conventional and nanotechnology-based strategies to achieve antimicrobial efficacy in dental biomaterials. Recent developments in the design and synthesis of antimicrobial peptides and antifouling zwitterionic polymers to effectively lessen the risks of antimicrobial drug resistance are also outlined in this review. Further, the role of contemporary strategies such as use of smart biomaterials, ionic solvent-based biomaterials and quorum quenchers incorporated biomaterials in the elimination of dental pathogens are described in detail. Lastly, we mentioned the approach of using polymers to print custom-made three-dimensional antibacterial dental products via additive manufacturing technologies. This review provides a critical perspective on the chemical, biomimetic, and engineering strategies intended for developing antimicrobial biomaterials that have the potential to substantially improve the dental health.

Taxonomic bias in AMP prediction of invertebrate peptides

Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here we tested if there is taxonomic bias in the prediction power in 10 tools with a total of 20 prediction algorithms in 19 invertebrate taxa, using a data set containing 1525 AMP and 3050 non-AMP sequences. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups. Based on the per-taxa performances and on the variation in performances across taxa we provide guidance in choosing the best-performing prediction tool for all assessed taxa, by listing the highest scoring tool for each of them.

LAMP2: a major update of the database linking antimicrobial peptides

Antimicrobial peptides (AMPs) have been regarded as a potential weapon to fight against drug-resistant bacteria, which is threating the globe. Thus, more and more AMPs had been designed or identified. There is a need to integrate them into a platform for researchers to facilitate investigation and analyze existing AMPs. The AMP database has become an important tool for the discovery and transformation of AMPs as agents. A database linking antimicrobial peptides (LAMPs), launched in 2013, serves as a comprehensive tool to supply exhaustive information of AMP on a single platform. LAMP2, an updated version of LAMP, holds 23 253 unique AMP sequences and expands to link 16 public AMP databases. In the current version, there are more than 50% (12 236) sequences only linking a single database and more than 45% of AMPs linking two or more database links. Additionally, updated categories based on primary structure, collection, composition, source and function have been integrated into LAMP2. Peptides in LAMP2 have been integrated in 8 major functional classes and 38 functional activities. More than 89% (20 909) of the peptides are experimentally validated peptides. A total of 1924 references were extracted and regarded as the evidence for supporting AMP activity and cytotoxicity. The updated version will be helpful to the scientific community.

Structure and regulation of the NK-lysin (1-4) and NK-lysin like (a and b) antimicrobial genes in rainbow trout (Oncorhynchus mykiss)

Nk-lysin (Nkl), an antimicrobial peptide (AMP) product of natural killer cells and cytotoxic T cells in mammals, has recently been characterized in a number of finfish species. In this study, we identified six genes with sequence homology to Nkl and characterized their patterns of mRNA expression and abundances in rainbow trout (Oncorhynchus mykiss). The cDNA sequences for the six Nkls encoded precursor peptides of 128-133 aa in length, and mature peptides of 109-111 aa in length. Genomic DNA of the nkl1-4 genes consisted of five exons and four introns, whereas the nkl-like a & b genes consisted of four exons and three introns. Chromosomal locations of these peptides show that nkl1 was located on chromosome arm 25q, whereas the other five nkl genes were clustered on chromosome arm 19q. Phylogenetic analysis revealed a conserved structure of Nkls among the teleosts and further protein sequence analyses suggests that all six nkl genes fall within the Nkl sub-family of the Saposin family of proteins. Patterns of tissue-specific mRNA expression were asymmetric among the six trout Nkl homologues, with nkl1, nkl3, and nkl-like a & b occurring in immune competent organs such as spleen, gill, intestine and kidney, as well as pineal gland, brain and oocytes. However, nkl2 and nkl4, showed primary abundances in brain, pineal gland and oocyte tissues. Using mRNA sequencing, in whole-body pools of juvenile trout fry (1 g bw) exposed to Flavobacterium psychrophilum infection, we observed modest up-regulation (2-3 fold) of five (nkl 2-4 and nkl-like a & b) of the six nkl mRNAs over the five-day post-challenge time-course. However, no upregulation could be recorded in spleen tissue measured by qPCR in juvenile trout (270 g bw). Using mRNA sequencing again, mRNA abundances were determined in gill of juvenile trout (~57.7 g bw) exposed to various aquaculture stressors. The results indicated that all six nkls (nkl1-4 and nkl-like a and nkl-like b) were downregulated when exposed to high temperature, and that nkl1 was significantly downregulated following salinity challenge. Overall, these newly characterized AMPs may contribute to host innate immunity as they are modulated following pathogen challenge and by physiological stressors.

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.

Attacins: A Promising Class of Insect Antimicrobial Peptides

Insects produce a large repertoire of antimicrobial peptides (AMPs) as the first line of defense against bacteria, viruses, fungi or parasites. These peptides are produced from a large precursor that contains a signal domain, which is cleaved in vivo to produce the mature protein with antimicrobial activity. At present, AMPs from insects include several families which can be classified as cecropins, ponericins, defensins, lebocins, drosocin, Metchnikowin, gloverins, diptericins and attacins according to their structure and/or function. This short review is focused on attacins, a class of glycine-rich peptides/proteins that have been first discovered in the cecropia moth (Hyalophora cecropia). They are a rather heterogeneous group of immunity-related proteins that exhibit an antimicrobial effect mainly against Gram-negative bacteria. Here, we discuss different attacin and attacin-like AMPs that have been discovered so far and analyze their structure and phylogeny. Special focus is given to the physiological importance and mechanism of action of attacins against microbial pathogens together with their potential pharmacological applications, emphasizing their roles as antimicrobials.

Antimicrobial Peptides and Cell-Penetrating Peptides for Treating Intracellular Bacterial Infections

Bacterial infections caused by intracellular pathogens are difficult to control. Conventional antibiotic therapies are often ineffective, as high doses are needed to increase the number of antibiotics that will cross the host cell membrane to act on the intracellular bacterium. Moreover, higher doses of antibiotics may lead to elevated severe toxic effects against host cells. In this context, antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs) have shown great potential to treat such infections by acting directly on the intracellular pathogenic bacterium or performing the delivery of cargos with antibacterial activities. Therefore, in this mini-review, we cover the main AMPs and CPPs described to date, aiming at intracellular bacterial infection treatment. Moreover, we discuss some of the proposed mechanisms of action for these peptide classes and their conjugation with other antimicrobials.

Combating Antimicrobial Resistance With New-To-Nature Lanthipeptides Created by Genetic Code Expansion

Due to the rapid emergence of multi-resistant bacterial strains in recent decades, the commercially available effective antibiotics are becoming increasingly limited. On the other hand, widespread antimicrobial peptides (AMPs) such as the lantibiotic nisin has been used worldwide for more than 40 years without the appearance of significant bacterial resistance. Lantibiotics are ribosomally synthesized antimicrobials generated by posttranslational modifications. Their biotechnological production is of particular interest to redesign natural scaffolds improving their pharmaceutical properties, which has great potential for therapeutic use in human medicine and other areas. However, conventional protein engineering methods are limited to 20 canonical amino acids prescribed by the genetic code. Therefore, the expansion of the genetic code as the most advanced approach in Synthetic Biology allows the addition of new amino acid building blocks (non-canonical amino acids, ncAAs) during protein translation. We now have solid proof-of-principle evidence that bioexpression with these novel building blocks enabled lantibiotics with chemical properties transcending those produced by natural evolution. The unique scaffolds with novel structural and functional properties are the result of this bioengineering. Here we will critically examine and evaluate the use of the expanded genetic code and its alternatives in lantibiotics research over the last 7 years. We anticipate that Synthetic Biology, using engineered lantibiotics and even more complex scaffolds will be a promising tool to address an urgent problem of antibiotic resistance, especially in a class of multi-drug resistant microbes known as superbugs.

In silico characterization of class II plant defensins from Arabidopsis thaliana

Defensins comprise a polyphyletic group of multifunctional defense peptides. Cis-defensins, also known as cysteine stabilized αβ (CSαβ) defensins, are one of the most ancient defense peptide families. In plants, these peptides have been divided into two classes, according to their precursor organization. Class I defensins are composed of the signal peptide and the mature sequence, while class II defensins have an additional C-terminal prodomain, which is proteolytically cleaved. Class II defensins have been described in Solanaceae and Poaceae species, indicating this class could be spread among all flowering plants. Here, a search by regular expression (RegEx) was applied to the Arabidopsis thaliana proteome, a model plant with more than 300 predicted defensin genes. Two sequences were identified, A7REG2 and A7REG4, which have a typical plant defensin structure and an additional C-terminal prodomain. TraVA database indicated they are expressed in flower, ovules and seeds, and being duplicated genes, this indicates they could be a result of a subfunctionalization process. The presence of class II defensin sequences in Brassicaceae and Solanaceae and evolutionary distance between them suggest class II defensins may be present in other eudicots. Discovery of class II defensins in other plants could shed some light on flower, ovules and seed physiology, as this class is expressed in these locations.

Detecting antimicrobial peptides by exploring the mutual information of their sequences

The rise of antibiotic resistance in pathogenic bacteria is a growing concern for every part of the world. The present study shows the prediction efficiency of mutual information for the classification of antimicrobial peptides. The proven role of antimicrobial peptides (AMPs) to fight against multidrug-resistant pathogens and AMP's low toxic properties laid the foundation of computational methods to play their role in detecting AMPs from non-AMPs. Mutual information vectors (MIV) were created for AMP/non-AMP sequences and then fed to different machine learning classifiers out of which a random forest (RF) classifier showed best results for predicting AMPs. Random forest classifiers were evaluated on benchmark datasets by 10-fold cross-validation. The proposed MIV-RF method showed better prediction accuracy, MCC (Matthews correlation coefficient), and AUC-ROC (Area Under The Curve-Receiver Operating Characteristics) than available methods for detecting AMPs.Communicated by Ramaswamy H. Sarma.

The reuse of public datasets in the life sciences: potential risks and rewards

The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.

An Update on Antimicrobial Peptides (AMPs) and Their Delivery Strategies for Wound Infections

Bacterial infections occur when wound healing fails to reach the final stage of healing, which is usually hindered by the presence of different pathogens. Different topical antimicrobial agents are used to inhibit bacterial growth due to antibiotic failure in reaching the infected site, which is accompanied very often by increased drug resistance and other side effects. In this review, we focus on antimicrobial peptides (AMPs), especially those with a high potential of efficacy against multidrug-resistant and biofilm-forming bacteria and fungi present in wound infections. Currently, different AMPs undergo preclinical and clinical phase to combat infection-related diseases. AMP dendrimers (AMPDs) have been mentioned as potent microbial agents. Various AMP delivery strategies that are used to combat infection and modulate the healing rate-such as polymers, scaffolds, films and wound dressings, and organic and inorganic nanoparticles-have been discussed as well. New technologies such as Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated protein (CRISPR-Cas) are taken into consideration as potential future tools for AMP delivery in skin therapy.

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

Variational Autoencoder for Generation of Antimicrobial Peptides

Over millennia, natural evolution has allowed for the emergence of countless biomolecules with highly specific roles within natural systems. As seen with peptides and proteins, often evolution produces molecules with a similar function but with variable amino acid composition and structure but diverging from a common ancestor, which can limit sequence diversity. Using antimicrobial peptides as a model biomolecule, we train a generative deep learning algorithm on a database of known antimicrobial peptides to generate novel peptide sequences with antimicrobial activity. Using a variational autoencoder, we are able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that show dose-responsive antimicrobial activity. These proof-of-concept studies demonstrate the potential for artificial intelligence-directed methods to generate new antimicrobial peptides and motivate their potential application toward peptide and protein design without the need for exhaustive screening of sequence libraries.

Emerging Contact-Killing Antibacterial Strategies for Developing Anti-Biofilm Dental Polymeric Restorative Materials

Polymeric materials are the first choice for restoring tooth cavities, bonding tooth-colored fillings, sealing root canal systems, and many other dental restorative applications. However, polymeric materials are highly susceptible to bacterial attachment and colonization, leading to dental diseases. Many approaches have been investigated to minimize the formation of biofilms over polymeric restorative materials and at the tooth/material interfaces. Among them, contact-killing compounds have shown promising results to inhibit dental biofilms. Contact-killing compounds can be immobilized within the polymer structure, delivering a long-lasting effect with no leaching or release, thus providing advantages compared to release-based materials. This review discusses cutting-edge research on the development of contact-killing compounds in dental restorative materials to target oral pathogens. Contact-killing compounds in resin composite restorations, dental adhesives, root canal sealers, denture-based materials, and crown cements have all demonstrated promising antibacterial properties. Contact-killing restorative materials have been found to effectively inhibit the growth and activities of several oral pathogens related to dental caries, periodontal diseases, endodontic, and fungal infections. Further laboratory optimization and clinical trials using translational models are needed to confirm the clinical applicability of this new generation of contact-killing dental restorative materials.

The value of antimicrobial peptides in the age of resistance

Accelerating growth and global expansion of antimicrobial resistance has deepened the need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear advantages over conventional antibiotics which include slower emergence of resistance, broad-spectrum antibiofilm activity, and the ability to favourably modulate the host immune response. Broad bacterial susceptibility to antimicrobial peptides offers an additional tool to expand knowledge about the evolution of antimicrobial resistance. Structural and functional limitations, combined with a stricter regulatory environment, have hampered the clinical translation of antimicrobial peptides as potential therapeutic agents. Existing computational and experimental tools attempt to ease the preclinical and clinical development of antimicrobial peptides as novel therapeutics. This Review identifies the benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens, highlights advances in the deployment of novel promising antimicrobial peptides, and underlines the needs and priorities in designing focused development strategies taking into account the most advanced tools available.

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks

The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.

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.

Antimicrobial peptide selection from Lippia spp leaf transcriptomes

Antimicrobial resistance is considered a health issue worldwide. This public health problem underscores the importance of searching for new antimicrobial molecules with different mechanisms of action. Leaf transcriptomes were used to search and develop synthetic antimicrobial peptides derived from mRNA sequences. The in silico search for new AMPs from the L. rotundifolia and L. alba transcriptomes allowed the identification of 120 putative peptide mRNA sequences. Eight of them fitted into optimal parameters and were translated and chemically synthesized antimicrobial peptides. Their biological activity was tested in both Gram-positive and Gram-negative bacteria against which they exhibited antibacterial activity. However, they showed an important hemolytic effect. Afterwards, two active peptides showing bactericidal activity isolated from each plant transcriptome tested were modified and modeled in 11 new variants to increase their antimicrobial activity and stability and to reduce or eliminate their hemolytic effect from their original peptides. The La-AMP1 (MSLLERKLLMHFLRV) the original peptide from L. alba showed a 52% hemolytic effect while the derived peptide La-AMP1a (GLMKLLRELLHMFSRVG) had its hemolytic effect reduced to 0.5% at 128 μg.mL^-1. Similarly, we observed that the original peptide from L. rotundifolia, Lr-AMP1 (MRIGLRFVLM), displayed a 71.5% hemolytic effect, while its derived peptide Lr-AMP1f (GSVLRAIMRMFAKLMG) showed 0% hemolysis at 128 μg.mL^-1, tested with fresh human erythrocytes. Our results indicate a promising method for the search for novel antimicrobial agents with reduced or zero hemolytic effect, as well as prediction and optimization of their activity from plant mRNA libraries.

Prediction and Characterization of Cationic Arginine-Rich Plant Antimicrobial Peptide SM-985 From Teosinte (Zea mays ssp. mexicana)

Antimicrobial peptides (AMPs) are effective against different plant pathogens and newly considered as part of plant defense systems. From prokaryotes to eukaryotes, AMPs can exist in all forms of life. SM-985 is a cationic AMP (CAMP) isolated from the cDNA library of Mexican teosinte (Zea mays ssp. mexicana). A computational prediction server running with different algorithms was used to screen the teosinte cDNA library for AMPs, and the SM-985 peptide was predicted as an AMP with high probability prediction values. SM-985 is an arginine-rich peptide and composed of 21 amino acids (MW: 2671.06 Da). The physicochemical properties of SM-985 are very promising as an AMP, including the net charge (+8), hydrophobicity ratio of 23%, Boman index of 5.19 kcal/mol, and isoelectric point of 12.95. The SM-985 peptide has amphipathic α-helix conformations. The antimicrobial activity of SM-985 was confirmed against six bacterial plant pathogens, and the MIC of SM-985 against Gram-positive indicators was 8 μM, while the MIC of SM-985 against Gram-negative indicators was 4 μM. The SM-985 interacting with the bacterial membrane and this interaction were examined by treatment of the bacterial indicators with FITC-SM-985 peptide, which showed a high binding affinity of SM-985 to the bacterial membrane (whether Gram-positive or Gram-negative). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images of the treated bacteria with SM-985 demonstrated cell membrane damage and cell lysis. In vivo antimicrobial activity was examined, and SM-985 prevented leaf spot disease infection caused by Pst DC3000 on Solanum lycopersicum. Moreover, SM-985 showed sensitivity to calcium chloride salt, which is a common feature of CAMPs.

EcDBS1R6: A novel cationic antimicrobial peptide derived from a signal peptide sequence

BACKGROUND

Bacterial infections represent a major worldwide health problem the antimicrobial peptides (AMPs) have been considered as potential alternative agents for treating these infections. Here we demonstrated the antimicrobial activity of EcDBS1R6, a peptide derived from a signal peptide sequence of Escherichia coli that we previously turned into an AMP by making changes through the Joker algorithm.

METHODS

Antimicrobial activity was measured by broth microdilution method. Membrane integrity was measured using fluorescent probes and through scanning electron microscopy imaging. A sliding window of truncated peptides was used to determine the EcDBS1R6 active core. Molecular dynamics in TFE/water environment was used to assess the EcDBS1R6 structure.

RESULTS

Signal peptides are known to naturally interact with membranes; however, the modifications introduced by Joker transformed this peptide into a membrane-active agent capable of killing bacteria. The C-terminus was unable to fold into an α-helix whereas its fragments showed poor or no antimicrobial activity, suggesting that the EcDBS1R6 antibacterial core was located at the helical N-terminus, corresponding to the signal peptide portion of the parent peptide.

CONCLUSION

The strategy of transforming signal peptides into AMPs appears to be promising and could be used to produce novel antimicrobial agents.

GENERAL SIGNIFICANCE

The process of transforming an inactive signal peptide into an antimicrobial peptide could open a new venue for creating new AMPs derived from signal peptides.

Reprogramming biological peptides to combat infectious diseases

With the rapid spread of resistance among parasites and bacterial pathogens, antibiotic-resistant infections have drawn much attention worldwide. Consequently, there is an urgent need to develop new strategies to treat neglected diseases and drug-resistant infections. Here, we outline several new strategies that have been developed to counter pathogenic microorganisms by designing and constructing antimicrobial peptides (AMPs). In addition to traditional discovery and design mechanisms guided by chemical biology, synthetic biology and computationally-based approaches offer useful tools for the discovery and generation of bioactive peptides. We believe that the convergence of such fields, coupled with systematic experimentation in animal models, will help translate biological peptides into the clinic. The future of anti-infective therapeutics is headed towards specifically designed molecules whose form is driven by computer-based frameworks. These molecules are selective, stable, and active at therapeutic doses.

Advancing antimicrobial strategies for managing oral biofilm infections

Effective control of oral biofilm infectious diseases represents a major global challenge. Microorganisms in biofilms exhibit increased drug tolerance compared with planktonic cells. The present review covers innovative antimicrobial strategies for controlling oral biofilm-related infections published predominantly over the past 5 years. Antimicrobial dental materials based on antimicrobial agent release, contact-killing and multi-functional strategies have been designed and synthesized for the prevention of initial bacterial attachment and subsequent biofilm formation on the tooth and material surface. Among the therapeutic approaches for managing biofilms in clinical practice, antimicrobial photodynamic therapy has emerged as an alternative to antimicrobial regimes and mechanical removal of biofilms, and cold atmospheric plasma shows significant advantages over conventional antimicrobial approaches. Nevertheless, more preclinical studies and appropriately designed and well-structured multi-center clinical trials are critically needed to obtain reliable comparative data. The acquired information will be helpful in identifying the most effective antibacterial solutions and the most optimal circumstances to utilize these strategies.

DRAMP 2.0, an updated data repository of antimicrobial peptides

Data Repository of Antimicrobial Peptides (DRAMP, http://dramp.cpu-bioinfor.org/) is an open-access comprehensive database containing general, patent and clinical antimicrobial peptides (AMPs). Currently DRAMP has been updated to version 2.0, it contains a total of 19,899 entries (newly added 2,550 entries), including 5,084 general entries, 14,739 patent entries, and 76 clinical entries. The update covers new entries, structures, annotations, classifications and downloads. Compared with APD and CAMP, DRAMP contains 14,040 (70.56% in DRAMP) non-overlapping sequences. In order to facilitate users to trace original references, PubMed_ID of references have been contained in activity information. The data of DRAMP can be downloaded by dataset and activity, and the website source code is also available on dedicatedly designed download webpage. Although thousands of AMPs have been reported, only a few parts have entered clinical stage. In the paper, we described several AMPs in clinical trials, including their properties, indications and clinicaltrials.gov identifiers. Finally, we provide the applications of DRAMP in the development of AMPs.