DAMPD: a manually curated antimicrobial peptide database

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.

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/ .

MFPPDB: a comprehensive multi-functional plant peptide database

Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.

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.

Milk-Derived Antimicrobial Peptides: Overview, Applications, and Future Perspectives

The growing consumer awareness towards healthy and safe food has reformed food processing strategies. Nowadays, food processors are aiming at natural, effective, safe, and low-cost substitutes for enhancing the shelf life of food products. Milk, besides being a rich source of nutrition for infants and adults, serves as a readily available source of precious functional peptides. Due to the existence of high genetic variability in milk proteins, there is a great possibility to get bioactive peptides with varied properties. Among other bioactive agents, milk-originated antimicrobial peptides (AMPs) are gaining interest as attractive and safe additive conferring extended shelf life to minimally processed foods. These peptides display broad-spectrum antagonistic activity against bacteria, fungi, viruses, and protozoans. Microbial proteolytic activity, extracellular peptidases, food-grade enzymes, and recombinant DNA technology application are among few strategies to tailor specific peptides from milk and enhance their production. These bioprotective agents have a promising future in addressing the global concern of food safety along with the possibility to be incorporated into the food matrix without compromising overall consumer acceptance. Additionally, in conformity to the current consumer demands, these AMPs also possess functional properties needed for value addition. This review attempts to present the basic properties, synthesis approaches, action mechanism, current status, and prospects of antimicrobial peptide application in food, dairy, and pharma industry along with their role in ensuring the safety and health of consumers.

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

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

LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network

Lactic acid bacteria antimicrobial peptides (LABAMPs) are a class of active polypeptide produced during the metabolic process of lactic acid bacteria, which can inhibit or kill pathogenic bacteria or spoilage bacteria in food. LABAMPs have broad application in important practical fields closely related to human beings, such as food production, efficient agricultural planting, and so on. However, screening for antimicrobial peptides by biological experiment researchers is time-consuming and laborious. Therefore, it is urgent to develop a model to predict LABAMPs. In this work, we design a graph convolutional neural network framework for identifying of LABAMPs. We build heterogeneous graph based on amino acids, tripeptide and their relationships and learn weights of a graph convolutional network (GCN). Our GCN iteratively completes the learning of embedded words and sequence weights in the graph under the supervision of inputting sequence labels. We applied 10-fold cross-validation experiment to two training datasets and acquired accuracy of 0.9163 and 0.9379 respectively. They are higher that of other machine learning and GNN algorithms. In an independent test dataset, accuracy of two datasets is 0.9130 and 0.9291, which are 1.08% and 1.57% higher than the best methods of other online webservers.

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.

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.

Biological Functions and Applications of Antimicrobial Peptides

Abstract:

Despite antimicrobial resistance, which is attributed to the misuse of broad-spectrum antibiotics, antibiotics can indiscriminately kill pathogenic and beneficial microorganisms. These events disrupt the delicate microbial balance in both humans and animals, leading to secondary infections and other negative effects. Antimicrobial peptides (AMPs) are functional natural biopolymers in plants and animals. Due to their excellent antimicrobial activities and absence of microbial resistance, AMPs have attracted enormous research attention. We reviewed the antibacterial, antifungal, antiviral, antiparasitic, as well as antitumor properties of AMPs and research progress on AMPs. In addition, we highlighted various recommendations and potential research areas for their progress and challenges in practical applications.

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.

Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective

The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter ("Die" score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.

Chemical modifications to increase the therapeutic potential of antimicrobial peptides

The continued use of antibiotics has been accompanied by the rapid emergence and spread of antibiotic-resistant strains of bacteria. Antimicrobial peptides (AMPs), also known as host defense peptides, show multiple features as an ideal antimicrobial agent, including potent, rapid, and broad-spectrum antimicrobial activity, low promotion of antimicrobial resistance, potent anti-biofilm activity, and lethality against metabolically inactive microorganisms. However, several crucial drawbacks constrain the use of AMPs as clinical drugs, e.g., liability in vivo, toxicity when used systemically, and high production costs. Based on recent findings and our own experiences, here we summarize some chemical modifications and key design strategies to increase the therapeutic potential of AMPs, including 1) enhancing antimicrobial activities, 2) improving in vivo effectiveness, and 3) reduction in toxicity, which may facilitate the design and optimization of AMPs for the development of drug candidates. We also discuss the present challenges in the optimization of AMPs and future concerns about the resistance and cross-resistance to AMPs in the development of AMPs as therapeutic drugs.

sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks

Short antimicrobial peptides (sAMPs) belong to a significant repertoire of antimicrobial agents and are known to possess enhanced antimicrobial activity, higher stability and less toxicity to human cells, as well as less complex than other large biological drugs. As these molecules are significantly important, herein, a prediction method for sAMPs (with a sequence length ≤ 30 residues) is proposed for accurate and efficient prediction of sAMPs instead of laborious and costly experimental approaches. Benchmark dataset was collected from a recently reported study and sequences were converted into three channel images comprising information related to the position, frequency and sum of 12 physiochemical features as the first, second and third channels, respectively. Two image-based deep neural networks (DNNs), i.e. RESNET-50 and VGG-16 were trained and evaluated using various metrics while a comparative analysis with previous techniques was also performed. Validation of sAMP-PFPDeep was also performed by using molecular docking based analysis. The results showed that VGG-16 provided more accurate results, i.e. 98.30% training accuracy and 87.37% testing accuracy for predicting sAMPs as compared to those of RESNET-50 having 96.14% training accuracy and 83.87% testing accuracy. However, the comparative analysis revealed that both these models outperformed previously reported state-of-the-art methods. Based on the results, it is concluded that sAMP-PFPDeep can help identify antimicrobial peptides with promising accuracy and efficiency. It can help biologists and scientists to identify antimicrobial peptides, by further aiding the computer-aided drug design and discovery, as well as virtual screening protocols against various pathologies. sAMP-PFPDeep is available at (https://github.com/WaqarHusain/sAMP-PFPDeep).

In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review

In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.

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.

Recombinant fusion protein by lysozyme and antibacterial peptide enhances ischemic wound healing via angiogenesis and reduction of inflammation in diabetic db/db mice

Background & aims

Lysozyme and antibacterial peptides have been reported to broad-spectrum antibacterial activity and can further improve wound healing. The aim of this study was to assess the effectiveness of a recombinant fusion protein created by combining lysozyme and an antibacterial peptide in forming new vessels and wound healing in an ischemic hind limb.

Methods

An ischemic hind limb model was established by isolation and ligation of the femoral artery in diabetic db/db mice. Cutaneous wounds were created with or without ischemia. Adductor muscles and wounds were treated with or without the fusion protein.

Results

The fusion protein accelerated ischemic diabetic wound healing and attenuated impairment of ischemic adductor muscle . Further, the fusion protein elevated expression of platelet derived growth factor (PDGF) and vascular endothelial growth factor (VEGF) protein and mRNA in ischemic adductor muscle, reduced levels of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) in serum and expression of phosphorylated nuclear factor κB (p-NF-κB) and p-IKB α in ischemic adductor. The fusion protein also enhanced levels of phosphorylated VEGF and PDGF receptors in the ischemic adductor muscles from diabetic db/db mice.

Conclusion

The data showed that the beneficial effects of the fusion protein on ischemic wound healing may be associated with angiogenesis and reduction of inflammatory response in the ischemic adductor muscles of diabetic db/db mice.

Antimicrobial/anticancer peptides: bioactive molecules and therapeutic agents

Antimicrobial peptides (AMPs) have been known as host-defense peptides. These cationic and amphipathic peptides are relatively short (∼5-50 L-amino acids) with molecular weight less than 10 kDa. AMPs have various roles including immunomodulatory, angiogenic and antitumor activities. Anticancer peptides (ACPs) are a main subset of AMPs as a novel therapeutic approach against tumor cells. The physicochemical properties of the ACPs influence their cell penetration, stability and efficiency of targeting. Up to now, several databases and web servers for in silico prediction of AMPs/ACPs have been established prior to the lab analysis. The present review focuses on the recent advancement about AMPs/ACPs activities including their in silico prediction by computational tools and their potential applications as therapeutic agents especially in cancer.

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

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.

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.

Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Progress in the development of antimicrobial peptide prediction tools

Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

Antimicrobial Peptides - Small but Mighty Weapons for Plants to Fight Phytopathogens

Antimicrobial Peptides (AMPs) have diverse structures, varied modes of actions, and can inhibit the growth of a wide range of pathogens at low concentrations. Plants are constantly under attack by a wide range of phytopathogens causing massive yield losses worldwide. To combat these pathogens, nature has armed plants with a battery of defense responses including Antimicrobial Peptides (AMPs). These peptides form a vital component of the two-tier plant defense system. They are constitutively expressed as part of the pre-existing first line of defense against pathogen entry. When a pathogen overcomes this barrier, it faces the inducible defense system, which responds to specific molecular or effector patterns by launching an arsenal of defense responses including the production of AMPs. This review emphasizes the structural and functional aspects of different plant-derived AMPs, their homology with AMPs from other organisms, and how their biotechnological potential could generate durable resistance in a wide range of crops against different classes of phytopathogens in an environmentally friendly way without phenotypic cost.

Little Antimicrobial Peptides with Big Therapeutic Roles

Antimicrobial Peptides (AMPs) are short amphipathic biological molecules generally with less than 100 amino acids. AMPs not only present high bioactivities against bacteria, fungi or protists-induced infections, but also play important roles in anticancer activity, immune response and inflammation regulation. AMPs are classified as ribosomally synthesized, non-ribosomally synthesized and post-translationally modified, non-ribosomally synthesized ones and several synthetic or semisynthetic peptides according to their synthesis with or without the involvement of ribosomes. The molecular characterization and bioactivity action mechanisms are summarized for several ribosomally synthesized AMPs and main non-ribosomally synthesized members (cyclopeptides, lipopeptides, glycopeptides, lipoglycopeptides). We also analyze challenges and new strategies to overcome drug resistance and application limitations for AMP discovery. In conclusion, the growing novel small molecular AMPs have huge therapeutic potentials of antibacterial, antiviral, anticancer and immunoregulatory bioactivities through new techniquesdriven drug discovery strategy including bioinformatics prediction, de novo rational design and biosynthesis.

From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs

The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.

Peptide from thaumatin plant protein exhibits selective anticandidal activity by inducing apoptosis via membrane receptor

Osmotin- and thaumatin-like proteins (OLPs and TLPs) have been associated with plant defense responses to different biotic stresses. In the present work, several in silico sequences from OLPs and TLPs were investigated by means of bioinformatics tools aiming to prospect for antimicrobial peptides. The peptide sequences chosen were further synthesized and characterized, and their activities and action mechanisms were assayed against some phytopathogenic fungi, bacteria and yeasts of clinical importance. From this survey approach, four peptide sequences (GDCKATSC, CPRALKVPGGCN, IVGQCPAKLKA, and CAADIVGQCPAKLK) were selected considering some chemical parameters commonly attributed to antimicrobial peptides. Antimicrobial assays showed that these peptides were unable to inhibit mycelial growth of phytopathogenic fungi and they did not affect bacterial cell growth. Nevertheless, significant inhibitory activity was found for CPRALKVPGGCN and CAADIVGQCPAKLK against Candida albicans and Saccharomyces cerevisiae. Fluorescence and scanning electron microscopy assays suggested that CAADIVGQCPAKLK did not damage the overall cell structure, or its activity was negligible on yeast membrane and cell wall integrity. However, it induced the production of reactive oxygen species (ROS) and apoptosis. Molecular docking analysis showed that CAADIVGQCPAKLK had strong affinity to interact with specific plasma membrane receptors of C. albicans and S. cerevisiae, which have been described as promoting the induction of apoptosis. The results indicate that CAADIVGQCPAKLK can be a valuable target for the development of a desired antimicrobial agent against the pathogen C. albicans.

Antimicrobial Peptides of Multicellular Organisms: My Perspective

Antimicrobial peptides of multicellular organisms were first characterized in the 1980s by investigators who felt that known systems of immunity could not explain what they observed: the resistance to bacterial infection of a Cecropia moth pupa lacking antibodies or lymphocytes (cecropins (Steiner 1981)), the potent microbicidal activity of neutrophils from a rabbit (defensins (Selsted et al. 1985)), and the healing of a wound on the skin of the African clawed frog without infection in a non-sterile aquarium (magainins (Zasloff 1987)). Since then AMPs have been discovered in diverse species of fungi, plants, and animals (Seshadri Sundararajan et al. 2012; Fan et al. 2016; Waghu et al. 2016; Wang et al. 2016). It is likely that we will discover that every multicellular organism expresses antimicrobial peptides as a key element of their immune system. Why are antimicrobial peptides so popular in Nature?

Comment on: ‘Empirical comparison of web-based antimicrobial peptide prediction tools’

Supplementary information: Supplementary data are available at Bioinformatics online.

StraPep: a structure database of bioactive peptides

Bioactive peptides, with a variety of biological activities and wide distribution in nature, have attracted great research interest in biological and medical fields, especially in pharmaceutical industry. The structural information of bioactive peptide is important for the development of peptide-based drugs. Many databases have been developed cataloguing bioactive peptides. However, to our knowledge, database dedicated to collect all the bioactive peptides with known structure is not available yet. Thus, we developed StraPep, a structure database of bioactive peptides. StraPep holds 3791 bioactive peptide structures, which belong to 1312 unique bioactive peptide sequences. About 905 out of 1312 (68%) bioactive peptides in StraPep contain disulfide bonds, which is significantly higher than that (21%) of PDB. Interestingly, 150 out of 616 (24%) bioactive peptides with three or more disulfide bonds form a structural motif known as cystine knot, which confers considerable structural stability on proteins and is an attractive scaffold for drug design. Detailed information of each peptide, including the experimental structure, the location of disulfide bonds, secondary structure, classification, post-translational modification and so on, has been provided. A wide range of user-friendly tools, such as browsing, sequence and structure-based searching and so on, has been incorporated into StraPep. We hope that this database will be helpful for the research community.

Database URL: http://isyslab.info/StraPep

Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

Background

Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model’s performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored.

Results

We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides.

Conclusions

The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5030-1) contains supplementary material, which is available to authorized users.

Peptide based antimicrobials: Design strategies and therapeutic potential

Therapeutic activity of antibiotics is noteworthy, as they are used in the treatment of microbial infections. Regardless of their utility, there has been a steep decrease in the number of drug candidates due to antibiotic resistance, an inevitable consequence of noncompliance with the full therapeutic regimen. A variety of resistant species like MDR (Multi-Drug Resistant), XDR (Extensively Drug-Resistant) and PDR (Pan Drug-Resistant) species have evolved, but discovery pipeline has already shown signs of getting dried up. Therefore, the need for newer antibiotics is of utmost priority to combat the microbial infections of future times. Peptides have some interesting features like minimal side effect, high tolerability and selectivity towards specific targets, which would help them successfully comply with the stringent safety standards set for clinical trials. In this review, we attempt to present the state of the art in the discovery of peptide-based antimicrobials from a design perspective.

In silico approaches for predicting the half-life of natural and modified peptides in blood

This paper describes a web server developed for designing therapeutic peptides with desired half-life in blood. In this study, we used 163 natural and 98 modified peptides whose half-life has been determined experimentally in mammalian blood, for developing in silico models. Firstly, models have been developed on 261 peptides containing natural and modified residues, using different chemical descriptors. The best model using 43 PaDEL descriptors got a maximum correlation of 0.692 between the predicted and the actual half-life peptides. Secondly, models were developed on 163 natural peptides using amino acid composition feature of peptides and achieved a maximum correlation of 0.643. Thirdly, models were developed on 163 natural peptides using chemical descriptors and attained a maximum correlation of 0.743 using 45 selected PaDEL descriptors. In order to assist researchers in the prediction and designing of half-life of peptides, the models developed have been integrated into PlifePred web server (http://webs.iiitd.edu.in//raghava/plifepred/).

Empirical comparison of web-based antimicrobial peptide prediction tools

Motivation

Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify.

Results

We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks.

Contact

gaberemu@ngha.med.sa or william-noble@uw.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Designing improved active peptides for therapeutic approaches against infectious diseases

Infectious diseases are one of the main causes of human morbidity and mortality. In the last few decades, pathogenic microorganisms' resistance to conventional drugs has been increasing, and it is now pinpointed as a major worldwide health concern. The need to search for new therapeutic options, as well as improved treatment outcomes, has therefore increased significantly, with biologically active peptides representing a new alternative. A substantial research effort is being dedicated towards their development, especially due to improved biocompatibility and target selectivity. However, the inherent limitations of peptide drugs are restricting their application. In this review, we summarize the current status of peptide drug development, focusing on antiviral and antimicrobial peptide activities, highlighting the design improvements needed, and those already being used, to overcome the drawbacks of the therapeutic application of biologically active peptides.

Computational resources and tools for antimicrobial peptides

Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure-activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.

PEPlife: A Repository of the Half-life of Peptides

Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

Methodology for identification of pore forming antimicrobial peptides from soy protein subunits β-conglycinin and glycinin

Antimicrobial peptides (AMPs) inactivate microbial cells through pore formation in cell membrane. Because of their different mode of action compared to antibiotics, AMPs can be effectively used to combat drug resistant bacteria in human health. AMPs can also be used to replace antibiotics in animal feed and immobilized on food packaging films. In this research, we developed a methodology based on mechanistic evaluation of peptide-lipid bilayer interaction to identify AMPs from soy protein. Production of AMPs from soy protein is an attractive, cost-saving alternative for commercial consideration, because soy protein is an abundant and common protein resource. This methodology is also applicable for identification of AMPs from any protein. Initial screening of peptide segments from soy glycinin (11S) and soy β-conglycinin (7S) subunits was based on their hydrophobicity, hydrophobic moment and net charge. Delicate balance between hydrophilic and hydrophobic interactions is necessary for pore formation. High hydrophobicity decreases the peptide solubility in aqueous phase whereas high hydrophilicity limits binding of the peptide to the bilayer. Out of several candidates chosen from the initial screening, two peptides satisfied the criteria for antimicrobial activity, viz. (i) lipid-peptide binding in surface state and (ii) pore formation in transmembrane state of the aggregate. This method of identification of antimicrobial activity via molecular dynamics simulation was shown to be robust in that it is insensitive to the number of peptides employed in the simulation, initial peptide structure and force field. Their antimicrobial activity against Listeria monocytogenes and Escherichia coli was further confirmed by spot-on-lawn test.

Antimicrobial peptides as novel anti-tuberculosis therapeutics

Tuberculosis (TB), a disease caused by the human pathogen Mycobacterium tuberculosis, has recently joined HIV/AIDS as the world's deadliest infectious disease, affecting around 9.6 million people worldwide in 2014. Of those, about 1.2 million died from the disease. Resistance acquisition to existing antibiotics, with the subsequent emergence of Multi-Drug Resistant mycobacteria strains, together with an increasing economic burden, has urged the development of new anti-TB drugs. In this scope, antimicrobial peptides (AMPs), which are small, cationic and amphipathic peptides that make part of the innate immune system, now arise as promising candidates for TB treatment. In this review, we analyze the potential of AMPs for this application. We address the mechanisms of action, advantages and disadvantages over conventional antibiotics and how problems associated with its use may be overcome to boost their therapeutic potential. Additionally, we address the challenges of translational development from benchside to bedside, evaluate the current development pipeline and analyze the expected global impact from a socio-economic standpoint. The quest for more efficient and more compliant anti-TB drugs, associated with the great therapeutic potential of emerging AMPs and the rising peptide market, provide an optimal environment for the emergence of AMPs as promising therapies. Still, their pharmacological properties need to be enhanced and manufacturing-associated issues need to be addressed.

DRAMP: a comprehensive data repository of antimicrobial peptides

The growing problem of antibiotic-resistant microorganisms results in an urgent need for substitutes to conventional antibiotics with novel modes of action and effective activities. Antimicrobial peptides (AMPs), produced by a wide variety of living organisms acting as a defense mechanism against invading pathogenic microbes, are considered to be such promising alternatives. AMPs display a broad spectrum of antimicrobial activity and a low propensity for developing resistance. Therefore, a thorough understanding of AMPs is essential to exploit them as antimicrobial drugs. Considering this, we developed a comprehensive user-friendly data repository of antimicrobial peptides (DRAMP), which holds 17349 antimicrobial sequences, including 4571 general AMPs, 12704 patented sequences and 74 peptides in drug development. Entries in the database have detailed annotations, especially detailed antimicrobial activity data (shown as target organism with MIC value) and structure information. Annotations also include accession numbers crosslinking to Pubmed, Swiss-prot and Protein Data Bank (PDB). The website of the database comes with easy-to-operate browsing as well as searching with sorting and filtering functionalities. Several useful sequence analysis tools are provided, including similarity search, sequence alignment and conserved domain search (CD-Search). DRAMP should be a useful resource for the development of novel antimicrobial peptide drugs.

Comparative functional properties of engineered cationic antimicrobial peptides consisting exclusively of tryptophan and either lysine or arginine

We previously reported a series of de novo engineered cationic antibiotic peptides (eCAPs) consisting exclusively of arginine and tryptophan (WR) that display potent activity against diverse multidrug-resistant (MDR) bacterial strains. In this study, we sought to examine the influence of arginine compared to lysine on antibacterial properties by direct comparison of the WR peptides (8-18 residues) with a parallel series of engineered peptides containing only lysine and tryptophan. WR and WK series were compared for antibacterial activity by bacterial killing and growth inhibition assays and for mechanism of peptide-bacteria interactions by surface plasmon resonance and flow cytometry. Mammalian cytotoxicity was also assessed by flow cytometry, haemolytic and tetrazolium-based assays. The shortest arginine-containing peptides (8 and 10 mers) displayed a statistically significant increase in activity compared to the analogous lysine-containing peptides. The WR and WK peptides achieved maximum antibacterial activity at the 12-mer peptide (WK12 or WR12). Further examination of antibacterial mechanisms of the optimally active 12-mer peptides using surface plasmon resonance and flow cytometry demonstrates stronger interactions with Pseudomonasaeruginosa, greater membrane permeabilizing activity, and lower inhibitory effects of divalent cations on activity and membrane permeabilization properties of WR12 compared to WK12 (P < 0.05). Importantly, WK12 and WR12 displayed similar negligible haemolytic and cytotoxic effects at peptide concentrations up to ten times the MIC or 20 times the minimum bactericidal concentration. Thus, arginine, compared to lysine, can indeed yield enhanced antibacterial activity to minimize the required length to achieve functional antimicrobial peptides.