Antimicrobial Peptides Sequences Research Articles

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.

PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction.

New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and experimental minimum inhibitory concentration (MIC) data as input. Sampling from distinct regions of the learned latent space allows for controllable generation of new AMP sequences with minimal input parameters. Extensive analysis of the PepVAE-generated sequences paired with antimicrobial activity prediction models supports this modular design framework as a promising system for development of novel AMPs, demonstrating controlled production of AMPs with experimental validation of predicted antimicrobial activity.

3D Printing of Antibacterial, Biocompatible, and Biomimetic Hybrid Aerogel-Based Scaffolds with Hierarchical Porosities via Integrating Antibacterial Peptide-Modified Silk Fibroin with Silica Nanostructure.

Scaffold-mediated tissue engineering has become a golden solution for the regeneration of damaged bone tissues that lack self-regeneration capability. A successful scaffold in bone tissue engineering comprises a multitude of suitable biological, microarchitectural, and mechanical properties acting as different signaling cues for the cells to mediate the new tissue formation. Therefore, careful design of bioactive scaffold macro- and microstructures in multiple length scales and biophysical properties fulfilling the tissue repair demands are necessary yet challenging to achieve. Herein, we have developed an antibacterial and biocompatible silica-silk fibroin (SF) gel-based ink through novel yet simple chemical approaches of sol-gel and self-assembly followed by processing the obtained gels as three-dimensional (3D) hybrid aerogel-based scaffolds exploiting the advanced materials design approaches of micro-extrusion-based 3D printing, and directional freeze-casting/drying approaches. As the main constituent of the hybrid biocompatible scaffold of this study, we used the SF extracted from Bombyx mori silkworm cocoon. However, to increase the cell responsivity and bactericidal efficiency of the final scaffold, thiol-ended antimicrobial and cell adhesive peptide sequence (SH-CM-RGD) was conjugated to silica-SF hybrid gels via covalent attachment using a spacer molecule through either preprint (prior to sol-gel) or during the post-printing steps on the previously printed silica-SF gel. In the next step, the hybrid Silica-SF-CM-RGD hydrogel ink was 3D-printed into the construct with interconnected porous structure with hierarchically organized porosity and a combination of several promising properties. Namely, due to the covalent linkage of the antibacterial peptide to the SF, the scaffold shows potent bactericidal efficiency toward Gram-positive and Gram-negative bacteria. Moreover, nanostructured silica components in the 3D-printed composites could intertwine with SF-CM-RGD to support the mechanical properties in the final scaffold and the final osteoconductivity of the scaffold. This study supports the promising properties of 3D-printed silica-SF-based hybrid aerogels constructs for repairing bone defect.

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.

HAMP: A Knowledge-base of Antimicrobial Peptides from Human Microbiome

Background: Antimicrobial peptides (AMPs) can defend the hosts against various pathogens and are found in almost every life form from microorganisms to humans. As the rapid increase of drug-resistant strains in recent years is presenting a serious challenge to healthcare, antimicrobial peptides (AMPs) can revolutionize the antimicrobial development against the drugresistant microbes. Objective: The objective was to encourage the study on the human microbiome towards the inhibition of drug-resistant bacteria by the development of a database containing antimicrobial peptides from the human microbiome. Methods: This database is an outcome of an extended analysis of human metagenome, involving the prediction of coding regions, extraction of peptides, prediction of antimicrobial peptides, and modeling their structure utilizing different in silico tools. Furthermore, an intelligent hash function-based query engine was designed to validate the novelty of specific candidate peptide over the reported Knowledge-base. Result and Discussion: This Knowledge-base currently focuses on antimicrobial peptide sequences (AMPs) predicted from the human microbiome along with their 3D structures modeled using various modeling and molecular dynamics approaches. It includes a total of 1087 unique AMPs from various body sites, with 454 AMPs from the oral cavity, 180 AMPs from the gastrointestinal tract, 42 AMPs from the skin, 12 AMPs from the airway, 6 AMPs from the urogenital tract and 393 AMPs from undefined body locations. A scoring matrix has been generated based on the similarity scores of the sequences that have been incorporated into the Knowledge-base. Furthermore, a Jmol applet is included in the website to help users visualize the 3D structures. Conclusion: The information and functions of the Knowledge-base can offer great help in finding novel antimicrobial drugs, especially towards finding inhibitors for drug-resistant bacteria. The HAMP is freely available at https://bioserver.iiita.ac.in/amp/index.html.

Monitoring Human Milk β-Casein Phosphorylation and O-Glycosylation Over Lactation Reveals Distinct Differences between the Proteome and Endogenous Peptidome.

Human milk is a vital biofluid containing a myriad of molecular components to ensure an infant’s best start at a healthy life. One key component of human milk is β-casein, a protein which is not only a structural constituent of casein micelles but also a source of bioactive, often antimicrobial, peptides contributing to milk’s endogenous peptidome. Importantly, post-translational modifications (PTMs) like phosphorylation and glycosylation typically affect the function of proteins and peptides; however, here our understanding of β-casein is critically limited. To uncover the scope of proteoforms and endogenous peptidoforms we utilized mass spectrometry (LC-MS/MS) to achieve in-depth longitudinal profiling of β-casein from human milk, studying two donors across 16 weeks of lactation. We not only observed changes in β-casein’s known protein and endogenous peptide phosphorylation, but also in previously unexplored O-glycosylation. This newly discovered PTM of β-casein may be important as it resides on known β-casein-derived antimicrobial peptide sequences.

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.

Compelling Evidence for the Activity of Antiviral Peptides against SARS-CoV-2.

Multiple outbreaks of epidemic and pandemic viral diseases have occurred in the last 20 years, including those caused by Ebola virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The emergence or re-emergence of such diseases has revealed the deficiency in our pipeline for the discovery and development of antiviral drugs. One promising solution is the extensive library of antimicrobial peptides (AMPs) produced by all eukaryotic organisms. AMPs are widely known for their activity against bacteria, but many possess additional antifungal, antiparasitic, insecticidal, anticancer, or antiviral activities. AMPs could therefore be suitable as leads for the development of new peptide-based antiviral drugs. Sixty therapeutic peptides had been approved by the end of 2018, with at least another 150 in preclinical or clinical development. Peptides undergoing clinical trials include analogs, mimetics, and natural AMPs. The advantages of AMPs include novel mechanisms of action that hinder the evolution of resistance, low molecular weight, low toxicity toward human cells but high specificity and efficacy, the latter enhanced by the optimization of AMP sequences. In this opinion article, we summarize the evidence supporting the efficacy of antiviral AMPs and discuss their potential to treat emerging viral diseases including COVID-19.

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.

β-Defensins from common goby (Pomatoschistus microps) and silver trevally (Pseudocaranx georgianus): Molecular characterization and phylogenetic analysis.

Antimicrobial peptides (AMPs) are biologically active molecules involved in host defense present in a variety of organisms. They arean integral component of innate immunity, forming a front line of defense against potential pathogens, including antibiotic-resistant ones. Fishes are proven to be a prospective source of AMPs as they are constantly being challenged by a variety of pathogens and the AMPs are reported to play an inevitable role in fish immunity. Among them, β-defensins form one of the most studied multifunctional peptides with early evolutionary history and recently being considered as host defense peptides. The present study highlights the first-ever report on β-defensin AMP sequences from common goby (Pomatoschistus microps) and silver trevally (Pseudocaranx georgianus). A 192bp cDNA fragment with an open reading frame encoding 63 amino acids (aa) comprising a 20 aa signal peptide region at the N-terminal was obtained from the mRNA of gill tissue of both P. microps and P. georgianus by RT-PCR. These peptide sequences when characterized in silico at the molecular level revealed a 43 aa cationic mature peptide with the signature intra-molecular disulphide bonded cysteine residue pattern ascertaining its β-defensin identity, further confirmed by phylogenetic analysis. The data collected will pave the way for further research on varied facets of the peptide-like, tissue level expressions, antimicrobial activities on commonly encountered pathogens, and its feasibility as atherapeutant in the aquaculture scenario.

Farfantepenaeus gene-encoded antimicrobial peptides: Identification, molecular characterization and gene expression in response to fungal infections

The aim of this study was to identify and characterize, at the molecular and transcriptional levels, sequences encoding the different members of the four families of shrimp antimicrobial peptides (AMPs) in species of the genus Farfantepenaeus. The identification of the AMP sequences was performed by in silico analysis as well as by molecular cloning and nucleotide sequencing. We identified all seven shrimp ALFs (ALF-A to ALF-G), both Type IIa and Type IIb crustins as well as two stylicins (STY1 and STY2) in Farfantepenaeus. Only two genes (PEN1/2 and PEN4) of the four-member penaeidin family (PEN1/2 to PEN5) were found and this is the first report of stylicins as well as of several additional members of ALFs, crustins and penaeidins in species of the genus Farfantepenaeus. All AMP genes have shown to be constitutively transcribed in the shrimp immune cells (hemocytes), except for ALF-G. Finally, the transcriptional profile of the different AMPs was assessed in the hemocytes of F. paulensis (pink shrimp) following an experimental infection with the opportunistic filamentous fungus Fusarium solani. We found that while the expression of ALF-B was induced at 24 h, the STY2 gene was down-regulated at 48 h post-challenge. These results provide evidence of the molecular diversity of AMPs from shrimp of the genus Farfantepenaeus in terms of sequences, biochemical properties and expression profiles in response to infectious diseases.

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.

Beneficial Impacts of Incorporating the Non-Natural Amino Acid Azulenyl-Alanine into the Trp-Rich Antimicrobial Peptide buCATHL4B.

Antimicrobial peptides (AMPs) present a promising scaffold for the development of potent antimicrobial agents. Substitution of tryptophan by non-natural amino acid Azulenyl-Alanine (AzAla) would allow studying the mechanism of action of AMPs by using unique properties of this amino acid, such as ability to be excited separately from tryptophan in a multi-Trp AMPs and environmental insensitivity. In this work, we investigate the effect of Trp→AzAla substitution in antimicrobial peptide buCATHL4B (contains three Trp side chains). We found that antimicrobial and bactericidal activity of the original peptide was preserved, while cytocompatibility with human cells and proteolytic stability was improved. We envision that AzAla will find applications as a tool for studies of the mechanism of action of AMPs. In addition, incorporation of this non-natural amino acid into AMP sequences could enhance their application properties.

Design, Synthesis and Evaluation of Antimicrobial Database-Derived Peptides Against Drug-Resistant Gram-Positive and Gram-Negative Pathogens

Bacterial drug resistance has become a serious problem worldwide as a consequence of long-term use and misuse of antibiotics. Current research trends indicate the development of anti-microbial peptides (AMPs) as a promising alternative to conventional antibiotics and to tackle antibiotic resistance. In the present work, novel short AMPs containing less than 15 amino acids with potential broad-spectrum activity are de-novo designed. A computational database-guided pipeline of rational analysis and modification is developed and utilized based on established principles of AMP activity such as charge, hydrophobicity, length, amino-acid frequency, charge density and hydrophobic moments. Potential broad-spectrum AMP sequences are designed based on a consecutive refinement strategy in which seed AMP sequences with desirable properties are predicted from scratch and then modified for possible improvement inactivity. Finally, four AMP sequences were designed, synthesized and tested for their efficacy in-vitro for broad spectrum anti-microbial activity against major drug resistant pathogens namely two Gram-positive organisms Staphylococcus aureus, Bacillus cereus and two Gram-negative pathogens Klebsiella aerogenes and Klebsiella pnuemoniae. Based on the inhibitory concentrations, two among the four designed peptides namely PEP01 (GKIMYILTKKS) and PEP03 (FGIKLRSVWKR) showed best results as broad-spectrum AMPs among which the sequence of PEP03 is further projected as a better candidate in terms of its predicted cell-penetrating and non-toxic nature.

Methanol Skin Mucus Extract of Mrigal (Cirrhinus mrigala) Fish Peptide Targeting Viral Particles of Infectious Pancreatic Necrosis Virus (IPNV) and Infectious Salmon Anemia Virus (ISAV): an in silico Approach

The teleost fish skin mucus acts as an important physical and biological barrier that prevents fish from the surrounding environment. Many studies reported the presence of various immunological molecules in fish skin mucus that involve in protection against invading microbes. In the present study, the skin mucus proteins of freshwater fish Cirrhinus mrigala (mrigal) were extracted using organic solvent (methanol) and further analyzed by liquid chromatography-tandem mass spectrometry (LC–MS/MS) to identify proteins by database retrieval. LC–MS/MS analysis revealed the presence of diverse proteins in the methanol skin mucus extract. The identified proteins were classified into biological process, cellular process and molecular functions by Gene Ontology (GO) enrichment analysis. A peptide was selected, modelled and compared with other antimicrobial peptides sequences through phylogenetic analysis and showed that the modelled peptide shared high similarity with Arminin-1 of Cnidaria animals. We also investigated the potentiality of the modelled peptide against Infectious Pancreatic Necrosis Virus sub viral particle and Infectious Salmon Anemia Virus hemagglutinin-esterase protein through protein-peptide docking using ClusPro. The docking results confirmed that the modelled peptide has good interactions with viral particles. Therefore, these results suggest that the modelled peptide molecule from C. mrigala methanol skin mucus extract can be further studied that aid in the development of novel peptide candidate for the control of aquaculture viral diseases.

Characteristics and therapeutic applications of antimicrobial peptides.

The demand for novel antimicrobial compounds is rapidly growing due to the phenomenon of antibiotic resistance in bacteria. In response, numerous alternative approaches are being taken including use of polymers, metals, combinatorial approaches, and antimicrobial peptides (AMPs). AMPs are a naturally occurring part of the immune system of all higher organisms and display remarkable broad-spectrum activity and high selectivity for bacterial cells over host cells. However, despite good activity and safety profiles, AMPs have struggled to find success in the clinic. In this review, we outline the fundamental properties of AMPs that make them effective antimicrobials and extend this into three main approaches being used to help AMPs become viable clinical options. These three approaches are the incorporation of non-natural amino acids into the AMP sequence to impart better pharmacological properties, the incorporation of AMPs in hydrogels, and the chemical modification of surfaces with AMPs for device applications. These approaches are being developed to enhance the biocompatibility, stability, and/or bioavailability of AMPs as clinical options.

Protein-Based Films Functionalized with a Truncated Antimicrobial Peptide Sequence Display Broad Antimicrobial Activity.

The increasing bacterial resistance to antibiotics is driving strong demand for new antimicrobial biomaterials. This work describes the fabrication of free-standing films exhibiting antimicrobial properties by combining, in the same polypeptide chain, an elastin-like recombinamer comprising 200 repetitions of the pentamer VPAVG (A200) and an 18-amino-acid truncated variant of the antimicrobial peptide BMAP-28, termed BMAP-18. The fusion protein BMAP-18A200 was overexpressed and conveniently purified by a simplified and scalable nonchromatographic process. Free-standing films of BMAP-18A200 demonstrated to be stable without requiring cross-linking agents and displayed high antimicrobial activity against skin pathogens including Gram-negative and Gram-positive bacteria as well as unicellular and filamentous fungi. The antimicrobial activity of the films was mediated by direct contact of cells with the film surface, resulting in compromised structural integrity of microbial cells. Furthermore, the BMAP-18A200 films showed no cytotoxicity on normal human cell lines (skin fibroblasts and keratinocytes). All of these results highlight the potential of these biotechnological multifunctional polymers as new drug-free materials to prevent and treat microbial infections.

AmPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool.

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. amPEPpy is implemented in Python 3 and is freely available through GitHub (https://github.com/tlawrence3/amPEPpy). Supplementary data are available at Bioinformatics online.

Molecular diversification of antimicrobial peptides from the wolf spider Lycosa sinensis venom based on peptidomic, transcriptomic, and bioinformatic analyses.

The venom of Lycosoidea spiders is a complex multicomponent mixture of neurotoxic peptides (main components) and antimicrobial peptides (AMPs) as minor components. In this study, we described the high-throughput identification and analysis of AMPs from Lycosa sinensis venom (named LS-AMPs) using a combination strategy that includes the following three different analysis approaches: (i) peptidomic analysis, namely reversed-phase high-performance liquid chromatography (RP-HPLC) separation plus top-down sequencing by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS); (ii) transcriptomic analysis, namely cDNA library construction plus DNA sequencing; (iii) bioinformatic analysis, namely analysis and prediction for molecular characters of LS-AMPs by the online biology databases. In total, 52 sequences of AMPs were identified from L. sinensis venom, and all AMPs can be categorized into eight different families according to phylogenetic analysis and sequence identity. This is the largest number of AMPs identified from a spider species so far. In the present study, we demonstrated molecular characteristics, such as complex precursor, N- and/or C-terminally truncated analogs, and C-terminal amidation of LS-AMPs from L. sinensis venom. This is a preliminary investigation on the molecular diversification of venom-derived AMPs from the wolf spider species (family Lycosidae), and a detailed investigation on the functional diversity of LS-AMPs will be preformed in the future.

IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides.

Antimicrobial peptides (AMPs) are at the focus of attention due to their therapeutic importance and developing computational tools for the identification of efficient antibiotics from the primary structure. Here, we utilized the 13CNMR spectral of amino acids and clustered them into various groups. These clusters were used to build feature vectors for the AMP sequences based on the composition, transition, and distribution of cluster members. These features, along with the physicochemical properties of AMPs were exploited to learn computational models to predict active AMPs solely from their sequences. Naïve Bayes (NB), k-nearest neighbors (KNN), support-vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to build the classification system using the collected AMP datasets from the CAMP, LAMP, ADAM, and AntiBP databases. Our results were validated and compared with the CAMP and ADAM prediction systems and indicated that the synergistic combination of the 13CNMR features with the physicochemical descriptors enables the proposed ensemble mechanism to improve the prediction performance of active AMP sequences. Our web-based AMP prediction platform, IAMPE, is available at http://cbb1.ut.ac.ir/.