CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides

Is It Possible to Create Antimicrobial Peptides Based on the Amyloidogenic Sequence of Ribosomal S1 Protein of P. aeruginosa?

The development and testing of new antimicrobial peptides (AMPs) represent an important milestone toward the development of new antimicrobial drugs that can inhibit the growth of pathogens and multidrug-resistant microorganisms such as Pseudomonas aeruginosa, Gram-negative bacteria. Most AMPs achieve these goals through mechanisms that disrupt the normal permeability of the cell membrane, which ultimately leads to the death of the pathogenic cell. Here, we developed a unique combination of a membrane penetrating peptide and peptides prone to amyloidogenesis to create hybrid peptide: "cell penetrating peptide + linker + amyloidogenic peptide". We evaluated the antimicrobial effects of two peptides that were developed from sequences with different propensities for amyloid formation. Among the two hybrid peptides, one was found with antibacterial activity comparable to antibiotic gentamicin sulfate. Our peptides showed no toxicity to eukaryotic cells. In addition, we evaluated the effect on the antimicrobial properties of amino acid substitutions in the non-amyloidogenic region of peptides. We compared the results with data on the predicted secondary structure, hydrophobicity, and antimicrobial properties of the original and modified peptides. In conclusion, our study demonstrates the promise of hybrid peptides based on amyloidogenic regions of the ribosomal S1 protein for the development of new antimicrobial drugs against P. aeruginosa.

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.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Toward insights on antimicrobial selectivity of host defense peptides via machine learning model interpretation

Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question).

Bioinformatic tools support decision-making in plant disease management

Food loss due to pathogens is a major concern in agriculture, requiring the need for advanced disease detection and prevention measures to minimize pathogen damage to plants. Novel bioinformatic tools have opened doors for the low-cost rapid identification of pathogens and prevention of disease. The number of these tools is growing fast and a comprehensive and comparative summary of these resources is currently lacking. Here, we review all current bioinformatic tools used to identify the mechanisms of pathogen pathogenicity, plant resistance protein identification, and the detection and treatment of plant disease. We compare functionality, data volume, data sources, performance, and applicability of all tools to provide a comprehensive toolbox for researchers in plant disease management.

Too Hot to Handle: Antibacterial Peptides Identified in Ghost Pepper

Capsicum spp. (hot peppers) demonstrate a range of interesting bioactive properties spanning anti-inflammatory, antioxidant, and antimicrobial activities. While several species within the genus are known to produce antimicrobial peptides (AMPs), AMP sequence mining of genomic data indicates this space remains largely unexplored. Herein, in silico AMP predictions were paired with peptidomics to identify novel AMPs from the interspecific hybrid ghost pepper (Capsicum chinense × frutescens). AMP prediction algorithms revealed 115 putative AMPs within the Capsicum chinense genome, of which 14 were identified in the aerial tissue peptidome. PepSAVI-MS, de novo sequencing, and complementary approaches were used to fully molecularly characterize two novel AMPs, CC-AMP1 and CC-AMP2, including elucidation of a pyroglutamic acid post-translational modification of CC-AMP1 and disulfide bond connectivity of both. Both CC-AMP1 and CC-AMP2 have little homology with known AMPs and exhibited low μM antimicrobial activity against Gram-negative bacteria, including Escherichia coli. These findings demonstrate the complementary nature of peptidomics, bioactivity-guided discovery, and bioinformatics-based investigations to characterize plant AMP profiles.

DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides

Stapled antimicrobial peptides are an emerging class of artificial cyclic peptide molecules which have antimicrobial activity and potent structure stability. We previously published the Data Repository of Antimicrobial Peptides (DRAMP) as a manually annotated and open-access database of antimicrobial peptides (AMPs). In the update of version 3.0, special emphasis was placed on the new development of stapled AMPs, and a subclass of specific AMPs was added to store information on these special chemically modified AMPs. To help design low toxicity AMPs, we also added the cytotoxicity property of AMPs, as well as the expansion of newly discovered AMP data. At present, DRAMP has been expanded and contains 22259 entries (2360 newly added), consisting of 5891 general entries, 16110 patent entries, 77 clinical entries and 181 stapled AMPs. A total of 263 entries have predicted structures, and more than 300 general entries have links to experimentally determined structures in the Protein Data Bank. The update also covers new annotations, statistics, categories, functions and download links. DRAMP is available online at http://dramp.cpu-bioinfor.org/.

amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool

SUMMARY

Antimicrobial peptides (AMPs) are promising alternative antimicrobial agents. Currently, however, portable, user-friendly and efficient methods for predicting AMP sequences from genome-scale data are not readily available. Here we present amPEPpy, an open-source, multi-threaded command-line application for predicting AMP sequences using a random forest classifier.

AVAILABILITY AND IMPLEMENTATION

amPEPpy is implemented in Python 3 and is freely available through GitHub (https://github.com/tlawrence3/amPEPpy).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features

Background

Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular organisms, and are involved in the first line of defense function. Recent studies showed that AMPs perpetuate great potential that is not limited to antimicrobial activity. They are also crucial regulators of host immune responses that can modulate a wide range of activities, such as immune regulation, wound healing, and apoptosis. However, a microorganism's ability to adapt and to resist existing antibiotics triggered the scientific community to develop alternatives to conventional antibiotics. Therefore, to address this issue, we proposed Co-AMPpred, an in silico-aided AMP prediction method based on compositional features of amino acid residues to classify AMPs and non-AMPs.

Results

In our study, we developed a prediction method that incorporates composition-based sequence and physicochemical features into various machine-learning algorithms. Then, the boruta feature-selection algorithm was used to identify discriminative biological features. Furthermore, we only used discriminative biological features to develop our model. Additionally, we performed a stratified tenfold cross-validation technique to validate the predictive performance of our AMP prediction model and evaluated on the independent holdout test dataset. A benchmark dataset was collected from previous studies to evaluate the predictive performance of our model.

Conclusions

Experimental results show that combining composition-based and physicochemical features outperformed existing methods on both the benchmark training dataset and a reduced training dataset. Finally, our proposed method achieved 80.8% accuracies and 0.871 area under the receiver operating characteristic curve by evaluating on independent test set. Our code and datasets are available at https://github.com/onkarS23/CoAMPpred.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04305-2.

Respiratory ß-2-Microglobulin exerts pH dependent antimicrobial activity

The respiratory tract is a major entry site for microbial pathogens. To combat bacterial infections, the immune system has various defense mechanisms at its disposal, including antimicrobial peptides (AMPs). To search for novel AMPs from the respiratory tract, a peptide library from human broncho-alveolar-lavage (BAL) fluid was screened for antimicrobial activity by radial diffusion assays allowing the efficient detection of antibacterial activity within a small sample size. After repeated testing-cycles and subsequent purification, we identified ß-2-microglobulin (B2M) in antibacterially active fractions. B2M belongs to the MHC-1 receptor complex present at the surface of nucleated cells. It is known to inhibit the growth of Listeria monocytogenes and Escherichia coli and to facilitate phagocytosis of Staphylococcus aureus. Using commercially available B2M we confirmed a dose-dependent inhibition of Pseudomonas aeruginosa and L. monocytogenes. To characterize AMP activity within the B2M sequence, peptide fragments of the molecule were tested for antimicrobial activity. Activity could be localized to the C-terminal part of B2M. Investigating pH dependency of the antimicrobial activity of B2M demonstrated an increased activity at pH values of 5.5 and below, a hallmark of infection and inflammation. Sytox green uptake into bacterial cells following the exposure to B2M was determined and revealed a pH-dependent loss of bacterial membrane integrity. TEM analysis showed areas of disrupted bacterial membranes in L. monocytogenes incubated with B2M and high amounts of lysed bacterial cells. In conclusion, B2M as part of a ubiquitous cell surface complex may represent a potent antimicrobial agent by interfering with bacterial membrane integrity.

AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

Machine learning designs non-hemolytic antimicrobial peptides

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.

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.

LJ, Franco Castrillón J, Rojas-Montoya M, Castaño Osorio JC. New Insect Host Defense Peptides (HDP) From Dung Beetle (Coleoptera: Scarabaeidae) Transcriptomes

The Coleoptera Scarabaeidae family is one of the most diverse groups of insects on the planet, which live in complex microbiological environments. Their immune systems have evolved diverse families of Host Defense Peptides (HDP) with strong antimicrobial and immunomodulatory activities. However, there are several peptide sequences that await discovery in this group of organisms. This would pave the way to identify molecules with promising therapeutic potential. This work retrieved two sources of information: 1) De-novo transcriptomic data from two species of neotropical Scarabaeidae (Dichotomius satanas and Ontophagus curvicornis); 2) Sequence data deposited in available databases. A Blast-based search was conducted against the transcriptomes with a subset of sequences representative of the HDP. This work reports 155 novel HDP sequences identified in nine transcriptomes from seven species of Coleoptera: D. satanas (n = 76; 49.03%), O. curvicornis (n = 23; 14.83%), (Trypoxylus dichotomus) (n = 18; 11.61%), (Onthophagus nigriventris) (n = 10; 6.45%), (Heterochelus sp) (n = 6; 3.87%), (Oxysternon conspicillatum) (n = 18; 11.61%), and (Popillia japonica) (n = 4; 2.58%). These sequences were identified based on similarity to known HDP insect families. New members of defensins (n = 58; 37.42%), cecropins (n = 18; 11.61%), attancins (n = 41; 26.45%), and coleoptericins (n = 38; 24.52%) were described based on their physicochemical and structural characteristics, as well as their sequence relationship to other insect HDPs. Therefore, the Scarabaeidae family is a complex and rich group of insects with a great diversity of antimicrobial peptides with potential antimicrobial activity.

Delivery by Dendritic Mesoporous Silica Nanoparticles Enhances the Antimicrobial Activity of a Napsin-Derived Peptide Against Intracellular Mycobacterium tuberculosis

Tuberculosis remains a serious global health problem causing 1.3 million deaths annually. The causative pathogen Mycobacterium tuberculosis (Mtb) has developed several mechanisms to evade the immune system and resistances to many conventional antibiotics, so that alternative treatment strategies are urgently needed. By isolation from bronchoalveolar lavage and peptide optimization, a new antimicrobial peptide named NapFab is discovered. While showing robust activity against extracellular Mtb, the activity of NapFab against intracellular bacteria is limited due to low intracellular availability. By loading NapFab onto dendritic mesoporous silica nanoparticles (DMSN) as a carrier system, cellular uptake, and consequently antimycobacterial activity against intracellular Mtb is significantly enhanced. Furthermore, using lattice light-sheet fluorescence microscopy, it can be shown that the peptide is gradually released from the DMSN inside living macrophages over time. By electron microscopy and tomography, it is demonstrated that peptide loaded DMSN are stored in vesicular structures in proximity to mycobacterial phagosomes inside the cells, but the nanoparticles are typically not in direct contact with the bacteria. Based on the combination of functional and live-cell imaging analyses, it is hypothesized that after being released from the DMSN NapFab is able to enter the bacterial phagosome and gain access to the bacilli.

Marine Bioactive Peptides in Supplements and Functional Foods - A Commercial Perspective

Many bioactive peptides have been described from marine sources and much marine biomass is still not explored or utilized in products. Marine peptides can be developed into a variety of products, and there is a significant interest in the use of bioactive peptides from marine sources for nutraceuticals or functional foods. We present here a mini-review collecting the knowledge about the value chain of bioactive peptides from marine sources used in nutraceuticals and functional foods. Many reports describe bioactive peptides from marine sources, but in order to make these available to the consumers in commercial products, it is important to connect the bioactivities associated with these peptides to commercial opportunities and possibilities. In this mini-review, we present challenges and opportunities for the commercial use of bioactive peptides in nutraceuticals and functional food products. We start the paper by introducing approaches for isolation and identification of bioactive peptides and candidates for functional foods. We further discuss market-driven innovation targeted to ensure that isolated peptides and suggested products are marketable and acceptable by targeted consumers. To increase the commercial potential and ensure the sustainability of the identified bioactive peptides and products, we discuss scalability, regulatory frameworks, production possibilities and the shift towards greener technologies. Finally, we discuss some commercial products from marine peptides within the functional food market. We discuss the placement of these products in the larger picture of the commercial sphere of functional food products from bioactive peptides.

Computational Methods and Tools in Antimicrobial Peptide Research

The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.

Interaction of Temporin-L Analogues with the E. coli FtsZ Protein

The research of new therapeutic agents to fight bacterial infections has recently focused on the investigation of antimicrobial peptides (AMPs), the most common weapon that all organisms produce to prevent invasion by external pathogens. Among AMPs, the amphibian Temporins constitute a well-known family with high antibacterial properties against Gram-positive and Gram-negative bacteria. In particular, Temporin-L was shown to affect bacterial cell division by inhibiting FtsZ, a tubulin-like protein involved in the crucial step of Z-ring formation at the beginning of the division process. As FtsZ represents a leading target for new antibacterial compounds, in this paper we investigated in detail the interaction of Temporin L with Escherichia coli FtsZ and designed two TL analogues in an attempt to increase peptide-protein interactions and to better understand the structural determinants leading to FtsZ inhibition. The results demonstrated that the TL analogues improved their binding to FtsZ, originating stable protein-peptide complexes. Functional studies showed that both peptides were endowed with a high capability of inhibiting both the enzymatic and polymerization activities of the protein. Moreover, the TL analogues were able to inhibit bacterial growth at low micromolar concentrations. These observations may open up the way to the development of novel peptide or peptidomimetic drugs tailored to bind FtsZ, hampering a crucial process of bacterial life that might be proposed for future pharmaceutical applications.

BING, a novel antimicrobial peptide isolated from Japanese medaka plasma, targets bacterial envelope stress response by suppressing cpxR expression

Antimicrobial peptides (AMPs) have emerged as a promising alternative to small molecule antibiotics. Although AMPs have previously been isolated in many organisms, efforts on the systematic identification of AMPs in fish have been lagging. Here, we collected peptides from the plasma of medaka (Oryzias latipes) fish. By using mass spectrometry, 6399 unique sequences were identified from the isolated peptides, among which 430 peptides were bioinformatically predicted to be potential AMPs. One of them, a thermostable 13-residue peptide named BING, shows a broad-spectrum toxicity against pathogenic bacteria including drug-resistant strains, at concentrations that presented relatively low toxicity to mammalian cell lines and medaka. Proteomic analysis indicated that BING treatment induced a deregulation of periplasmic peptidyl-prolyl isomerases in gram-negative bacteria. We observed that BING reduced the RNA level of cpxR, an upstream regulator of envelope stress responses. cpxR is known to play a crucial role in the development of antimicrobial resistance, including the regulation of genes involved in drug efflux. BING downregulated the expression of efflux pump components mexB, mexY and oprM in P. aeruginosa and significantly synergised the toxicity of antibiotics towards these bacteria. In addition, exposure to sublethal doses of BING delayed the development of antibiotic resistance. To our knowledge, BING is the first AMP shown to suppress cpxR expression in Gram-negative bacteria. This discovery highlights the cpxR pathway as a potential antimicrobial target.

Web repositories of natural agents promote pests and pathogenic microbes management

The grand challenge to meet the increasing demands for food by a rapidly growing global population requires protecting crops from pests. Natural active substances play a significant role in the sustainable pests and pathogenic microbes management. In recent years, natural products- (NPs), antimicrobial peptides- (AMPs), medicinal plant- and plant essential oils (EOs)-related online resources have greatly facilitated the development of pests and pathogenic microbes control agents in an efficient and economical manner. However, a comprehensive comparison, analysis and summary of these existing web resources are still lacking. Here, we surveyed these databases of NPs, AMPs, medicinal plants and plant EOs with insecticidal, antibacterial, antiviral and antifungal activity, and we compared their functionality, data volume, data sources and applicability. We comprehensively discussed the limitation of these web resources. This study provides a toolbox for bench scientists working in the pesticide, botany, biomedical and pharmaceutical engineering fields. The aim of the review is to hope that these web resources will facilitate the discovery and development of potential active ingredients of pests and pathogenic microbes control agents.

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

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

Analysis of Antimicrobial Peptide Metabolome of Bacterial Endophyte Isolated From Traditionally Used Medicinal Plant Millettia pachycarpa Benth

Increasing prevalence of antimicrobial resistance (AMR) has posed a major health concern worldwide, and the addition of new antimicrobial agents is diminishing due to overexploitation of plants and microbial resources. Inevitably, alternative sources and new strategies are needed to find novel biomolecules to counter AMR and pandemic circumstances. The association of plants with microorganisms is one basic natural interaction that involves the exchange of biomolecules. Such a symbiotic relationship might affect the respective bio-chemical properties and production of secondary metabolites in the host and microbes. Furthermore, the discovery of taxol and taxane from an endophytic fungus, Taxomyces andreanae from Taxus wallachiana, has stimulated much research on endophytes from medicinal plants. A gram-positive endophytic bacterium, Paenibacillus peoriae IBSD35, was isolated from the stem of Millettia pachycarpa Benth. It is a rod-shaped, motile, gram-positive, and endospore-forming bacteria. It is neutralophilic as per Joint Genome Institute’s (JGI) IMG system analysis. The plant was selected based on its ethnobotany history of traditional uses and highly insecticidal properties. Bioactive molecules were purified from P. peoriae IBSD35 culture broth using 70% ammonium sulfate and column chromatography techniques. The biomolecule was enriched to 151.72-fold and the yield percentage was 0.05. Peoriaerin II, a highly potent and broad-spectrum antimicrobial peptide against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans ATCC 10231 was isolated. LC-MS sequencing revealed that its N-terminal is methionine. It has four negatively charged residues (Asp + Glu) and a total number of two positively charged residues (Arg + Lys). Its molecular weight is 4,685.13 Da. It is linked to an LC-MS/MS inferred biosynthetic gene cluster with accession number A0A2S6P0H9, and blastp has shown it is 82.4% similar to fusaricidin synthetase of Paenibacillus polymyxa SC2. The 3D structure conformation of the BGC and AMP were predicted using SWISS MODEL homology modeling. Therefore, combining both genomic and proteomic results obtained from P. peoriae IBSD35, associated with M. pachycarpa Benth., will substantially increase the understanding of antimicrobial peptides and assist to uncover novel biological agents.

Differential interactions of the antimicrobial peptide, RQ18, with phospholipids and cholesterol modulate its selectivity for microorganism membranes

BACKGROUND

Antimicrobial peptides (AMPs) are molecules with potential application for the treatment of microorganism infections. We, herein, describe the structure, activity, and mechanism of action of RQ18, an α-helical AMP that displays antimicrobial activity against Gram-positive and Gram-negative bacteria, and yeasts from the Candida genus.

METHODS

A physicochemical-guided design assisted by computer tools was used to obtain our lead peptide candidate, named RQ18. This peptide was assayed against Gram-positive and Gram-negative bacteria, yeasts, and mammalian cells to determine its selectivity index. The secondary structure and the mechanism of action of RQ18 were investigated using circular dichroism, large unilamellar vesicles, and molecular dynamic simulations.

RESULTS

RQ18 was not cytotoxic to human lung fibroblasts, peripheral blood mononuclear cells, red blood cells, or Vero cells at MIC values, exhibiting a high selectivity index. Circular dichroism analysis and molecular dynamic simulations revealed that RQ18 presents varying structural profiles in aqueous solution, TFE/water mixtures, SDS micelles, and lipid bilayers. The peptide was virtually unable to release carboxyfluorescein from large unilamellar vesicles composed of POPC/cholesterol, model that mimics the eukaryotic membrane, indicating that vesicles' net charges and the presence of cholesterol may be related with RQ18 selectivity for bacterial and fungal cell surfaces.

CONCLUSIONS

RQ18 was characterized as a membrane-active peptide with dual antibacterial and antifungal activities, without compromising mammalian cells viability, thus reinforcing its therapeutic application.

GENERAL SIGNIFICANCE

These results provide further insight into the complex process of AMPs interaction with biological membranes, in special with systems that mimic prokaryotic and eukaryotic cell surfaces.

Molecular Insights into the Role of Cysteine-Rich Peptides in Induced Resistance to Fusarium oxysporum Infection in Tomato Based on Transcriptome Profiling

Cysteine-rich peptides (CRPs) play an important role in plant physiology. However, their role in resistance induced by biogenic elicitors remains poorly understood. Using whole-genome transcriptome sequencing and our CRP search algorithm, we analyzed the repertoire of CRPs in tomato Solanum lycopersicum L. in response to Fusarium oxysporum infection and elicitors from F. sambucinum. We revealed 106 putative CRP transcripts belonging to different families of antimicrobial peptides (AMPs), signaling peptides (RALFs), and peptides with non-defense functions (Major pollen allergen of Olea europaea (Ole e 1 and 6), Maternally Expressed Gene (MEG), Epidermal Patterning Factor (EPF)), as well as pathogenesis-related proteins of families 1 and 4 (PR-1 and 4). We discovered a novel type of 10-Cys-containing hevein-like AMPs named SlHev1, which was up-regulated both by infection and elicitors. Transcript profiling showed that F. oxysporum infection and F. sambucinum elicitors changed the expression levels of different overlapping sets of CRP genes, suggesting the diversification of functions in CRP families. We showed that non-specific lipid transfer proteins (nsLTPs) and snakins mostly contribute to the response of tomato plants to the infection and the elicitors. The involvement of CRPs with non-defense function in stress reactions was also demonstrated. The results obtained shed light on the mode of action of F. sambucinum elicitors and the role of CRP families in the immune response in tomato.

A novel antibacterial peptide recognition algorithm based on BERT

As the best substitute for antibiotics, antimicrobial peptides (AMPs) have important research significance. Due to the high cost and difficulty of experimental methods for identifying AMPs, more and more researches are focused on using computational methods to solve this problem. Most of the existing calculation methods can identify AMPs through the sequence itself, but there is still room for improvement in recognition accuracy, and there is a problem that the constructed model cannot be universal in each dataset. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved gratifying results. It also has great application prospects in the field of AMP recognition and prediction. In this paper, we apply the pre-training strategy to the model training of AMP classifiers and propose a novel recognition algorithm. Our model is constructed based on the BERT model, pre-trained with the protein data from UniProt, and then fine-tuned and evaluated on six AMP datasets with large differences. Our model is superior to the existing methods and achieves the goal of accurate identification of datasets with small sample size. We try different word segmentation methods for peptide chains and prove the influence of pre-training steps and balancing datasets on the recognition effect. We find that pre-training on a large number of diverse AMP data, followed by fine-tuning on new data, is beneficial for capturing both new data's specific features and common features between AMP sequences. Finally, we construct a new AMP dataset, on which we train a general AMP recognition model.

Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides

Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides

The rapid spread of multi-drug resistant microbes has lead researchers to discover natural alternative remedies such as antimicrobial peptides (AMPs). In the first line of defense, AMPs display a broad spectrum of potent activity against multi-resistant pathogenic bacteria, viruses, fungi, and even cancer. AMPs can be further characterised into families according to amino acid composition, secondary structure, and function. However, despite recent advancements in rapid computational methods for AMP prediction from various mammalian, aquatic, and terrestrial species, there is limited information regarding their presence, functional roles, and family type from marine macroalgae. In this paper, we present a promising two-tier ensemble of heterogeneous machine learning models that integrates seven well-known machine learning classifiers to predict AMPs from macroalgae. The first tier of the ensemble consists of a suite of binary classifiers that identify AMPs from protein sequence data which are then forwarded to a second-tier multi-class ensemble to characterise their functional family type. The two-tier ensemble was successfully used to identify 39 putative AMP sequences in 12 macroalgae species from three different phyla groups. The approach we describe is not limited to AMPs and can also be applied to search sequence data for other types of proteins.

Structure and antimicrobial activity of NCR169, a nodule-specific cysteine-rich peptide of Medicago truncatula

A model legume, Medicago truncatula, has over 600 nodule-specific cysteine-rich (NCR) peptides required for symbiosis with rhizobia. Among them, NCR169, an essential factor for establishing symbiosis, has four cysteine residues that are indispensable for its function. However, knowledge of NCR169 structure and mechanism of action is still lacking. In this study, we solved two NMR structures of NCR169 caused by different disulfide linkage patterns. We show that both structures have a consensus C-terminal β-sheet attached to an extended N-terminal region with dissimilar features; one moves widely, whereas the other is relatively stapled. We further revealed that the disulfide bonds of NCR169 contribute to its structural stability and solubility. Regarding the function, one of the NCR169 oxidized forms could bind to negatively charged bacterial phospholipids. Furthermore, the positively charged lysine-rich region of NCR169 may be responsible for its antimicrobial activity against Escherichia coli and Sinorhizobium meliloti. This active region was disordered even in the phospholipid bound state, suggesting that the disordered conformation of this region is key to its function. Morphological observations suggested the mechanism of action of NCR169 on bacteria. The present study on NCR169 provides new insights into the structure and function of NCR peptides.

Genomic analysis of novel Yarrowia-like yeast symbionts associated with the carrion-feeding burying beetle Nicrophorus vespilloides

Background

Mutualistic interactions with microbes can help insects adapt to extreme environments and unusual diets. An intriguing example is the burying beetle Nicrophorus vespilloides, which feeds and reproduces on small vertebrate carcasses. Its fungal microbiome is dominated by yeasts that potentially facilitate carcass utilization by producing digestive enzymes, eliminating cadaver-associated toxic volatiles (that would otherwise attract competitors), and releasing antimicrobials to sanitize the microenvironment. Some of these yeasts are closely related to the biotechnologically important species Yarrowia lipolytica.

Results

To investigate the roles of these Yarrowia-like yeast (YLY) strains in more detail, we selected five strains from two different phylogenetic clades for third-generation sequencing and genome analysis. The first clade, represented by strain B02, has a 20-Mb genome containing ~ 6400 predicted protein-coding genes. The second clade, represented by strain C11, has a 25-Mb genome containing ~ 6300 predicted protein-coding genes, and extensive intraspecific variability within the ITS–D1/D2 rDNA region commonly used for species assignments. Phenotypic microarray analysis revealed that both YLY strains were able to utilize a diverse range of carbon and nitrogen sources (including microbial metabolites associated with putrefaction), and can grow in environments with extreme pH and salt concentrations.

Conclusions

The genomic characterization of five yeast strains isolated from N. vespilloides resulted in the identification of strains potentially representing new YLY species. Given their abundance in the beetle hindgut, and dominant growth on beetle-prepared carcasses, the analysis of these strains has revealed the genetic basis of a potential symbiotic relationship between yeasts and burying beetles that facilitates carcass digestion and preservation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07597-z.

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

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

Peptides to Tackle Leishmaniasis: Current Status and Future Directions

Peptide-based drugs are an attractive class of therapeutic agents, recently recognized by the pharmaceutical industry. These molecules are currently being used in the development of innovative therapies for diverse health conditions, including tropical diseases such as leishmaniasis. Despite its socioeconomic influence on public health, leishmaniasis remains long-neglected and categorized as a poverty-related disease, with limited treatment options. Peptides with antileishmanial effects encountered to date are a structurally heterogeneous group, which can be found in different natural sources—amphibians, reptiles, insects, bacteria, marine organisms, mammals, plants, and others—or inspired by natural toxins or proteins. This review details the biochemical and structural characteristics of over one hundred peptides and their potential use as molecular frameworks for the design of antileishmanial drug leads. Additionally, we detail the main chemical modifications or substitutions of amino acid residues carried out in the peptide sequence, and their implications in the development of antileishmanial candidates for clinical trials. Our bibliographic research highlights that the action of leishmanicidal peptides has been evaluated mainly using in vitro assays, with a special emphasis on the promastigote stage. In light of these findings, and considering the advances in the successful application of peptides in leishmaniasis chemotherapy, possible approaches and future directions are discussed here.

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.

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

The rumen eukaryotome is a source of novel antimicrobial peptides with therapeutic potential

BACKGROUND

The rise of microbial antibiotic resistance is a leading threat to the health of the human population. As such, finding new approaches to tackle these microbes, including development of novel antibiotics is vital.

RESULTS

In this study, we mined a rumen eukaryotic metatranscriptomic library for novel Antimicrobial peptides (AMPs) using computational approaches and thereafter characterised the therapeutic potential of the AMPs. We identified a total of 208 potentially novel AMPs from the ruminal eukaryotome, and characterised one of those, namely Lubelisin. Lubelisin (GIVAWFWRLAR) is an α-helical peptide, 11 amino acid long with theoretical molecular weight of 1373.76 D. In the presence of Lubelisin, strains of methicillin-resistant Staphylococcus aureus (MRSA) USA300 and EMRSA-15 were killed within 30 min of exposure with ≥103 and 104 CFU/mL reduction in viable cells respectively. Cytotoxicity of Lubelisin against both human and sheep erythrocytes was low resulting in a therapeutic index of 0.43. Membrane permeabilisation assays using propidium iodide alongside transmission electron microscopy revealed that cytoplasmic membrane damage may contribute to the antimicrobial activities of Lubelisin.

CONCLUSIONS

We demonstrate that the rumen eukaryotome is a viable source for the discovery of antimicrobial molecules for the treatment of bacterial infections and further development of these may provide part of the potential solution to the ongoing problem of antimicrobial resistance. The role of these AMPs in the ecological warfare within the rumen is also currently unknown.

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides

Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments, it is necessary to create design approaches with higher precision and selectivity toward resistant targets. Previously, we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2, a bidirectional conditional generative adversarial network (BiCGAN)-based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data-driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate candidates that are novel, diverse, and tailored for specific applications, making it an efficient AMP design tool.

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

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

Deep Learning for Novel Antimicrobial Peptide Design

Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic. The need for new antibiotics is high, while the arsenal of available agents is decreasing, especially for the treatment of infections by Gram-negative bacteria like Escherichia coli. Antimicrobial peptides (AMPs) are offering a promising route for novel antibiotic development and deep learning techniques can be utilised for successful AMP design. In this study, a long short-term memory (LSTM) generative model and a bidirectional LSTM classification model were constructed to design short novel AMP sequences with potential antibacterial activity against E. coli. Two versions of the generative model and six versions of the classification model were trained and optimised using Bayesian hyperparameter optimisation. These models were used to generate sets of short novel sequences that were classified as antimicrobial or non-antimicrobial. The validation accuracies of the classification models were 81.6–88.9% and the novel AMPs were classified as antimicrobial with accuracies of 70.6–91.7%. Predicted three-dimensional conformations of selected short AMPs exhibited the alpha-helical structure with amphipathic surfaces. This demonstrates that LSTMs are effective tools for generating novel AMPs against targeted bacteria and could be utilised in the search for new antibiotics leads.

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

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

Modeling novel Anti-Viral peptides (AVPs) with in-silico docking simulations against corona virus

The havoc created by Corona virus has been dealt with using various integrative approaches adopted by laboratories through-out the world. Use of anti-viral peptides (AVPs) although new but has shown tremendous potential against many pathogens.

Previously AVPs have been designed against spike protein of corona virus which is the major entry mediating molecule. Using various in-silico strategies, in this research work AVPs have been modeled against lesser studied viral proteins namely ORF7a protein, Envelope protein (E), Nucleoprotein (N), and Non-Structural protein (Nsp1 and Nsp2). The predicted AVPs have been docked against various host as well as viral proteins. The interaction of small AVPs seems capable of interfering with binding between viral protein and its host counterpart. Therefore, these AVPs can act as a deterrent against novel corona virus, which requires further validation through laboratory techniques.

Addition of Dairy Lipids and Probiotic Lactobacillus fermentum in Infant Formulas Modulates Proteolysis and Lipolysis With Moderate Consequences on Gut Physiology and Metabolism in Yucatan Piglets

Breast milk is the gold standard in neonatal nutrition, but most infants are fed infant formulas in which lipids are usually of plant origin. The addition of dairy lipids and/or milk fat globule membrane extracts in formulas improves their composition with beneficial consequences on protein and lipid digestion. The probiotic Lactobacillus fermentum (Lf) was reported to reduce transit time in rat pups, which may also improve digestion. This study aimed to investigate the effects of the addition of dairy lipids in formulas, with or without Lf, on protein and lipid digestion and on gut physiology and metabolism. Piglets were suckled from postnatal days 2 to 28, with formulas containing either plant lipids (PL), a half-half mixture of plant and dairy lipids (DL), or this mixture supplemented with Lf (DL+Lf). At day 28, piglets were euthanized 90 min after their last feeding. Microstructure of digesta did not differ among formulas. Gastric proteolysis was increased (P < 0.01) in DL and DL+Lf (21.9 ± 2.1 and 22.6 ± 1.3%, respectively) compared with PL (17.3 ± 0.6%) and the residual proportion of gastric intact caseins decreased (p < 0.01) in DL+Lf (5.4 ± 2.5%) compared with PL and DL (10.6 ± 3.1% and 21.8 ± 6.8%, respectively). Peptide diversity in ileum and colon digesta was lower in PL compared to DL and DL+Lf. DL and DL+Lf displayed an increased (p < 0.01) proportion of diacylglycerol/cholesterol in jejunum and ileum digesta compared to PL and tended (p = 0.07) to have lower triglyceride/total lipid ratio in ileum DL+Lf (0.019 ± 0.003) as compared to PL (0.045 ± 0.011). The percentage of endocrine tissue and the number of islets in the pancreas were decreased (p < 0.05) in DL+Lf compared with DL. DL+Lf displayed a beneficial effect on host defenses [increased goblet cell density in jejunum (p < 0.05)] and a trophic effect [increased duodenal (p = 0.09) and jejunal (p < 0.05) weights]. Altogether, our results demonstrate that the addition of dairy lipids and probiotic Lf in infant formula modulated protein and lipid digestion, with consequences on lipid profile and with beneficial, although moderate, physiological effects.

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.

Molecular engineering of antimicrobial peptides: microbial targets, peptide motifs and translation opportunities

The global public health threat of antimicrobial resistance has led the scientific community to highly engage into research on alternative strategies to the traditional small molecule therapeutics. Here, we review one of the most popular alternatives amongst basic and applied research scientists, synthetic antimicrobial peptides. The ease of peptide chemical synthesis combined with emerging engineering principles and potent broad-spectrum activity, including against multidrug-resistant strains, has motivated intense scientific focus on these compounds for the past decade. This global effort has resulted in significant advances in our understanding of peptide antimicrobial activity at the molecular scale. Recent evidence of molecular targets other than the microbial lipid membrane, and efforts towards consensus antimicrobial peptide motifs, have supported the rise of molecular engineering approaches and design tools, including machine learning. Beyond molecular concepts, supramolecular chemistry has been lately added to the debate; and helped unravel the impact of peptide self-assembly on activity, including on biofilms and secondary targets, while providing new directions in pharmaceutical formulation through taking advantage of peptide self-assembled nanostructures. We argue that these basic research advances constitute a solid basis for promising industry translation of rationally designed synthetic peptide antimicrobials, not only as novel drugs against multidrug-resistant strains but also as components of emerging antimicrobial biomaterials. This perspective is supported by recent developments of innovative peptide-based and peptide-carrier nanobiomaterials that we also review.

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

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

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.