APD: the Antimicrobial Peptide Database

Antimicrobial peptides: Opportunities and challenges in overcoming resistance

Antibiotic resistance represents a global health threat, challenging the efficacy of traditional antimicrobial agents and necessitating innovative approaches to combat infectious diseases. Among these alternatives, antimicrobial peptides have emerged as promising candidates against resistant pathogens. Unlike traditional antibiotics with only one target, these peptides can use different mechanisms to destroy bacteria, with low toxicity to mammalian cells compared to many conventional antibiotics. Antimicrobial peptides (AMPs) have encouraging antibacterial properties and are currently employed in the clinical treatment of pathogen infection, cancer, wound healing, cosmetics, or biotechnology. This review summarizes the mechanisms of antimicrobial peptides against bacteria, discusses the mechanisms of drug resistance, the limitations and challenges of AMPs in peptide drug applications for combating drug-resistant bacterial infections, and strategies to enhance their capabilities.

In silico-driven identification and experimental confirmation of antifungal proteins (AFPs) against Candidaalbicans

Mycoses infect millions of people annually across the world. The most common mycosis agent, Candida albicans is responsible for a great deal of illness and death. C. albicans infection is becoming more widespread and the current antifungals polyenes, triazoles, and echinocandins are less efficient against it. Investigating antifungal peptides (AFPs) as therapeutic is gaining momentum. Therefore, we used MALDI-TOF/MS analysis to identify AFPs and protein-protein docking to analyze their interactions with the C. albicans target protein. Some microorganisms with strong antifungal action against C. albicans were selected for the isolation of AFPs. Using MALDI-TOF/MS, we identified 3 AFPs Chitin binding protein (ACW83017.1; Bacillus licheniformis), the bifunctional protein GlmU (BBQ13478.1; Stenotrophomonas maltophilia), and zinc metalloproteinase aureolysin (BBA25172.1; Staphylococcus aureus). These AFPs showed robust interactions with C. albicans target protein Sap5. We deciphered some important residues in identified APFs and highlighted interaction with Sap5 through hydrogen bonds, protein-protein interactions, and salt bridges using protein-protein docking and MD simulations. The three discovered AFPs-Sap5 complexes exhibit different levels of stability, as seen by the RMSD analysis and interaction patterns. Among protein-protein interactions, the remarkable stability of the BBQ25172.1-2QZX complex highlights the role of salt bridges and hydrogen bonds. Identified AFPs could be further studied for developing successful antifungal candidates and peptide-based new antifungal therapeutic strategies as fresh insights into addressing antifungal resistance also.

Exploring pathological link between antimicrobial and amyloid peptides

Amyloid peptides (AMYs) and antimicrobial peptides (AMPs) are considered as the two distinct families of peptides, characterized by their unique sequences, structures, biological functions, and specific pathological targets. However, accumulating evidence has revealed intriguing pathological connections between these peptide families in the context of microbial infection and neurodegenerative diseases. Some AMYs and AMPs share certain structural and functional characteristics, including the ability to self-assemble, the presence of β-sheet-rich structures, and membrane-disrupting mechanisms. These shared features enable AMYs to possess antimicrobial activity and AMPs to acquire amyloidogenic properties. Despite limited studies on AMYs-AMPs systems, the cross-seeding phenomenon between AMYs and AMPs has emerged as a crucial factor in the bidirectional communication between the pathogenesis of neurodegenerative diseases and host defense against microbial infections. In this review, we examine recent developments in the potential interplay between AMYs and AMPs, as well as their pathological implications for both infectious and neurodegenerative diseases. By discussing the current progress and challenges in this emerging field, this account aims to inspire further research and investments to enhance our understanding of the intricate molecular crosstalk between AMYs and AMPs. This knowledge holds great promise for the development of innovative therapies to combat both microbial infections and neurodegenerative disorders.

LC-MS/MS profiling and analysis of Bacillus licheniformis extracellular proteins for antifungal potential against Candida albicans

Candida albicans, a significant human pathogenic fungus, employs hydrolytic proteases for host invasion. Conventional antifungal agents are reported with resistance issues from around the world. This study investigates the role of Bacillus licheniformis extracellular proteins (ECP) as effective antifungal peptides (AFPs). The aim was to identify and characterize the ECP of B. licheniformis through LC-MS/MS and bioinformatics analysis. LC-MS/MS analysis identified 326 proteins with 69 putative ECP, further analyzed in silico. Of these, 21 peptides exhibited antifungal properties revealed by classAMP tool and are predominantly anionic. Peptide-protein docking revealed interactions between AFPs like Peptide chain release factor 1 (Q65DV1_Seq1: SASEQLSDAK) and Putative carboxy peptidase (Q65IF0_Seq7: SDSSLEDQDFILESK) with C. albicans virulent SAP5 proteins (PDB ID 2QZX), forming hydrogen bonds and significant Pi-Pi interactions. The identification of B. licheniformis ECP is the novelty of the study that sheds light on their antifungal potential. The identified AFPs, particularly those interacting with bonafide pharmaceutical targets SAP5 of C. albicans represent promising avenues for the development of antifungal treatments with AFPs that could be the pursuit of a novel therapeutic strategy against C. albicans. SIGNIFICANCE OF STUDY: The purpose of this work was to carry out proteomic profiling of the secretome of B. licheniformis. Previously, the efficacy of Bacillus licheniformis extracellular proteins against Candida albicans was investigated and documented in a recently communicated manuscript, showcasing the antifungal activity of these proteins. In order to achieve high-throughput identification of ES (Excretory-secretory) proteins, the utilization of liquid chromatography tandem mass spectrometry (LC-MS) was utilized. There was a lack of comprehensive research on AFPs in B. licheniformis, nevertheless. The proteins secreted by B. licheniformis in liquid medium were initially discovered using liquid chromatography-tandem mass spectrometry (LC-MS) analysis and identification in order to immediately characterize the unidentified active metabolites in fermentation broth.

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

Investigation of cytotoxic effect and action mechanism of a synthetic peptide derivative of rabbit cathelicidin against MDA-MB-231 breast cancer cell line

Antimicrobial peptides (AMPs) have sparked significant interest as potential anti-cancer agents, thereby becoming a focal point in pursuing novel cancer-fighting strategies. These peptides possess distinctive properties, underscoring the importance of developing more potent and selectively targeted versions with diverse mechanisms of action against human cancer cells. Such advancements would offer notable advantages compared to existing cancer therapies. This research aimed to examine the toxicity and selectivity of the nrCap18 peptide in both cancer and normal cell lines. Furthermore, the rate of cellular death was assessed using apoptosis and acridine orange/ethidium bromide (AO/EB) double staining at three distinct incubation times. Additionally, the impact of this peptide on the cancer cell cycle and migration was evaluated, and ultimately, the expression of cyclin-dependent kinase 4/6 (CDK4/6) genes was investigated. The results obtained from the study demonstrated significant toxicity and selectivity in cancer cells compared to normal cells. Moreover, a strong progressive increase in cell death was observed over time. Furthermore, the peptide exhibited the ability to halt the progression of cancer cells in the G1 phase of the cell cycle and impede their migration by suppressing the expression of CDK4/6 genes.

AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features

Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Computational exploration of novel antimicrobial modalities targeting fucose-binding lectins and ribosomes in Mycobacterium smegmatis using tRNA-encoded peptides

tRNA-Encoded Peptides (tREPs), encoded by small open reading frames (smORFs) within tRNA genes, have recently emerged as a new class of functional peptides exhibiting antiparasitic activity. The discovery of tREPs has led to a re-evaluation of the role of tRNAs in biology and has expanded our understanding of the genetic code. This presents an immense, unexplored potential in the realm of tRNA-peptide interactions, paving the way for groundbreaking discoveries and innovative applications in various biological functions. This study explores the antimicrobial potential of tREPs against protein targets by employing a computational method that uses verified data sources and highly recognized predictive algorithms to provide a sorted list of likely antimicrobial peptides, which were then filtered for toxicity, cell permeability, allergenicity and half-life. These peptides were then docked with screened protein targets and computationally validated using molecular dynamics (MD) simulations for 150 ns and the binding free energy was estimated. The peptides Pep2 (VVLWRKPRVRKTG) and Pep6 (HRLRLRRRKPWW) exhibited good binding affinities of -110.5 +/- 2.5 and -129.0 +/- 3.9, respectively, with RMSD values of 0.4 and 0.25 nm against the fucose-binding lectin (7NEF) and the 30S ribosome of Mycobacterium smegmatis (5O5J) protein targets. The 7NEF-Pep2 and 5O5J-Pep6 complexes indicated higher negative binding free energies of -52.55 kcal/mol and -55.52 kcal/mol respectively, as calculated by Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA). Thus, the tREPs derived peptides designed as a part of this study, provide novel approaches for potential anti-bacterial therapeutic modalities.Communicated by Ramaswamy H. Sarma.

SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition

It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP .

Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications

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

Equinins as Novel Broad-Spectrum Antimicrobial Peptides Isolated from the Cnidarian Actinia equina (Linnaeus, 1758)

Sea anemones are valuable for therapeutic research as a diversified source of bioactive molecules, due to their diverse bioactive molecules linked to predation and defence mechanisms involving toxins and antimicrobial peptides. Acid extracts from Actinia equina tentacles and body were examined for antibacterial activity against Gram-positive, Gram-negative bacteria, and fungi. The peptide fractions showed interesting minimum inhibitory concentration (MIC) values (up to 0.125 µg/mL) against the tested pathogens. Further investigation and characterization of tentacle acid extracts with significant antimicrobial activity led to the purification of peptides through reverse phase chromatography on solid phase and HPLC. Broad-spectrum antimicrobial peptide activity was found in 40% acetonitrile fractions. The resulting peptides had a molecular mass of 2612.91 and 3934.827 Da and MIC ranging from 0.06 to 0.20 mg/mL. Sequencing revealed similarities to AMPs found in amphibians, fish, and Cnidaria, with anti-Gram+, Gram-, antifungal, candidacidal, anti-methicillin-resistant Staphylococcus aureus, carbapenemase-producing, vancomycin-resistant bacteria, and multi-drug resistant activity. Peptides 6.2 and 7.3, named Equinin A and B, respectively, were synthesized and evaluated in vitro towards the above-mentioned bacterial pathogens. Equinin B exerted interesting antibacterial activity (MIC and bactericidal concentrations of 1 mg/mL and 0.25 mg/mL, respectively) and gene organization supporting its potential in applied research.

ACP-DRL: an anticancer peptides recognition method based on deep representation learning

Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.

AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention

Antimicrobial peptides (AMPs) are small molecular polypeptides that can be widely used in the prevention and treatment of microbial infections. Although many computational models have been proposed to help identify AMPs, a high-performance and interpretable model is still lacking. In this study, new benchmark data sets are collected and processed, and a stacking deep architecture named AMPpred-MFA is carefully designed to discover and identify AMPs. Multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (LSTM) network and a convolutional neural network (CNN). The effectiveness of AMPpred-MFA is verified through five independent tests conducted in batches. Experimental results show that AMPpred-MFA achieves a state-of-the-art performance. The visualization interpretability analyses and ablation experiments offer a further understanding of the model behavior and performance, validating the importance of our feature representation and stacking architecture, especially the multihead attention mechanism. Therefore, AMPpred-MFA can be considered a reliable and efficient approach to understanding and predicting AMPs.

Derivation of a novel antimicrobial peptide from the Red Sea Brine Pools modified to enhance its anticancer activity against U2OS cells

Cancer associated drug resistance is a major cause for cancer aggravation, particularly as conventional therapies have presented limited efficiency, low specificity, resulting in long term deleterious side effects. Peptide based drugs have emerged as potential alternative cancer treatment tools due to their selectivity, ease of design and synthesis, safety profile, and low cost of manufacturing. In this study, we utilized the Red Sea metagenomics database, generated during AUC/KAUST Red Sea microbiome project, to derive a viable anticancer peptide (ACP). We generated a set of peptide hits from our library that shared similar composition to ACPs. A peptide with a homeodomain was selected, modified to improve its anticancer properties, verified to maintain high anticancer properties, and processed for further in-silico prediction of structure and function. The peptide's anticancer properties were then assessed in vitro on osteosarcoma U2OS cells, through cytotoxicity assay (MTT assay), scratch-wound healing assay, apoptosis/necrosis detection assay (Annexin/PI assay), RNA expression analysis of Caspase 3, KI67 and Survivin, and protein expression of PARP1. L929 mouse fibroblasts were also assessed for cytotoxicity treatment. In addition, the antimicrobial activity of the peptide was also examined on E coli and S. aureus, as sample representative species of the human bacterial microbiome, by examining viability, disk diffusion, morphological assessment, and hemolytic analysis. We observed a dose dependent cytotoxic response from peptide treatment of U2OS, with a higher tolerance in L929s. Wound closure was debilitated in cells exposed to the peptide, while annexin fluorescent imaging suggested peptide treatment caused apoptosis as a major mode of cell death. Caspase 3 gene expression was not altered, while KI67 and Survivin were both downregulated in peptide treated cells. Additionally, PARP-1 protein analysis showed a decrease in expression with peptide exposure. The peptide exhibited minimal antimicrobial activity on critical human microbiome species E. coli and S. aureus, with a low inhibition rate, maintenance of structural morphology and minimal hemolytic impact. These findings suggest our novel peptide displayed preliminary ACP properties against U2OS cells, through limited specificity, while triggering apoptosis as a primary mode of cell death and while having minimal impact on the microbiological species E. coli and S. aureus.

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Introduction

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.

Methods

N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.

Results

The models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.

Discussion

Hydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

Antimicrobial peptides: An alternative to traditional antibiotics

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

EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features

Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Interaction of designed cationic antimicrobial peptides with the outer membrane of gram-negative bacteria

The outer membrane (OM) is a hallmark feature of gram-negative bacteria that provides the species with heightened resistance against antibiotic threats while cationic antimicrobial peptides (CAPs) are natural antibiotics broadly recognized for their ability to disrupt bacterial membranes. It has been well-established that lipopolysaccharides present on the OM are among major targets of CAP activity against gram-negative species. Here we investigate how the relative distribution of charged residues along the primary peptide sequence, in conjunction with its overall hydrophobicity, affects such peptide-OM interactions in the natural CAP Ponericin W1. Using a designed peptide library derived from Ponericin W1, we determined that the consecutive placement of Lys residues at the peptide N- or C-terminus (ex. "PonN": KKKKKKWLGSALIGALLPSVVGLFQ) enhances peptide binding affinity to OM lipopolysaccharides compared to constructs where Lys residues are interspersed throughout the primary sequence (ex. "PonAmp": WLKKALKIGAKLLPSVVKLFKGSGQ). Antimicrobial activity against multidrug resistant strains of Pseudomonas aeruginosa was similarly found to be highest among Lys-clustered sequences. Our findings suggest that while native Ponericin W1 exerts its initial activity at the OM, Lys-clustering may be a promising means to enhance potency towards this interface, thereby augmenting peptide entry and activity at the IM, with apparent advantage against multidrug-resistant species.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

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

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

Cell-free protein synthesis system: A new frontier for sustainable biotechnology-based products

Cell-free protein synthesis (CFPS) system is an innovative technology with a wide range of potential applications that could challenge current thinking and provide solutions to environmental and health issues. CFPS system has been demonstrated to be a successful way of producing biomolecules in a variety of applications, including the biomedical industry. Although there are still obstacles to overcome, its ease of use, versatility, and capacity for integration with other technologies open the door for it to continue serving as a vital instrument in synthetic biology research and industry. In this review, we mainly focus on the cell-free based platform for various product productions. Moreover, the challenges in the bio-therapeutic aspect using cell-free systems and their future prospective for the improvement and sustainability of the cell free systems.

iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model

As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.

The antimicrobial peptide database is 20 years old: Recent developments and future directions

In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.

Fuse feeds as one: cross-modal framework for general identification of AMPs

Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.

Antimicrobial Peptide Synergies for Fighting Infectious Diseases

Antimicrobial peptides (AMPs) are essential elements of thehost defense system. Characterized by heterogenous structures and broad-spectrumaction, they are promising candidates for combating multidrug resistance. Thecombined use of AMPs with other antimicrobial agents provides a new arsenal ofdrugs with synergistic action, thereby overcoming the drawback of monotherapiesduring infections. AMPs kill microbes via pore formation, thus inhibitingintracellular functions. This mechanism of action by AMPs is an advantage overantibiotics as it hinders the development of drug resistance. The synergisticeffect of AMPs will allow the repurposing of conventional antimicrobials andenhance their clinical outcomes, reduce toxicity, and, most significantly,prevent the development of resistance. In this review, various synergies ofAMPs with antimicrobials and miscellaneous agents are discussed. The effect ofstructural diversity and chemical modification on AMP properties is firstaddressed and then different combinations that can lead to synergistic action,whether this combination is between AMPs and antimicrobials, or AMPs andmiscellaneous compounds, are attended. This review can serve as guidance whenredesigning and repurposing the use of AMPs in combination with other antimicrobialagents for enhanced clinical outcomes.

Identification of Potential Bioactive Peptides in Sheep Milk Kefir through Peptidomic Analysis at Different Fermentation Times

Sheep farming is an important socioeconomic activity in most Mediterranean countries, particularly Spain, where it contributes added value to rural areas. Sheep milk is used in Spain mainly for making cheese, but it can be used also for making other dairy products, such as the lactic-alcoholic fermentation product known as kefir. Dairy products have health benefits because, among other reasons, they contain molecules with biological activity. In this work, we performed a proteomics strategy to identify the peptidome, i.e., the set of peptides contained in sheep milk kefir fermented for four different periods of time, aiming to understand changes in the pattern of digestion of milk proteins, as well as to identify potential bioactive peptides. In total, we identified 1942 peptides coming from 11 different proteins, and found that the unique peptides differed qualitatively among samples and their numbers increased along the fermentation time. These changes were supported by the increase in ethanol, lactic acid, and D-galactose concentrations, as well as proteolytic activity, as the fermentation progressed. By searching in databases, we found that 78 of the identified peptides, all belonging to caseins, had potential biological activity. Of these, 62 were not previously found in any milk kefir from other animal species. This is the first peptidomic study of sheep milk kefir comprising time-course comparison.

Characterization of a novel peptide mined from the Red Sea brine pools and modified to enhance its anticancer activity

Drug resistance is a major cause of the inefficacy of conventional cancer therapies, and often accompanied by severe side effects. Thus, there is an urgent need to develop novel drugs with low cytotoxicity, high selectivity and minimal acquired chemical resistance. Peptide-based drugs (less than 0.5 kDa) have emerged as a potential approach to address these issues due to their high specificity and potent anticancer activity. In this study, we developed a support vector machine model (SVM) to detect the potential anticancer properties of novel peptides by scanning the American University in Cairo (AUC) Red Sea metagenomics library. We identified a novel 37-mer antimicrobial peptide through SVM pipeline analysis and characterized its anticancer potential through in silico cross-examination. The peptide sequence was further modified to enhance its anticancer activity, analyzed for gene ontology, and subsequently synthesized. To evaluate the anticancer properties of the modified 37-mer peptide, we assessed its effect on the viability and morphology of SNU449, HepG2, SKOV3, and HeLa cells, using an MTT assay. Additionally, we evaluated the migration capabilities of SNU449 and SKOV3 cells using a scratch-wound healing assay. The targeted selectivity of the modified peptide was examined by evaluating its hemolytic activity on human erythrocytes. Treatment with the peptide significantly reduced cell viability and had a critical impact on the morphology of hepatocellular carcinoma (SNU449 and HepG2), and ovarian cancer (SKOV3) cells, with a marginal effect on cervical cancer cell lines (HeLa). The viability of a human fibroblast cell line (1Br-hTERT) was also significantly reduced by peptide treatment, as were the proliferation and migration abilities of SNU449 and SKOV3 cells. The annexin V assay revealed programmed cell death (apoptosis) as one of the potential cellular death pathways in SNU449 cells upon peptide treatment. Finally, the peptide exhibited antimicrobial effects on both gram-positive and gram-negative bacterial strains. The findings presented here suggest the potential of our novel peptide as a potent anticancer and antimicrobial agent.

Designing Potent Anticancer Peptides by Aurein 1.2 Key Residues Mutation and Catenate Cell-Penetrating Peptide

Purpose

Aurein 1.2 (Aur) peptide is known for possessing anticancer characteristics devoid of conventional therapeutics side effects. For improving Aur peptide anticancer functionality, different anticancer peptides were constructed based on Aur peptide through targeting two separate strategies, including (1) sequence-based mutations and (2) adding a cell-penetrating peptide linker.

Methods

The study was approached by designing three different analogs of Aur, including (a) Aur mutant (Aurm), (b) Aur with N-terminal polyarginine linker (R5-Aur), and (c) Aurm with R5 (R5-Aurm). Computational molecular dynamics simulations clearly showed higher structural stability of R5-Aur and R5-Aurm compared to Aur, solely. The α-helical properties of R5-Aur and R5-Aurm were protected during 500 ns simulations in water solution while no such structural conservation was seen for Aur in silico.

Results

The results of the current study highlight response to one of the main challenges of cancer therapy through selective invasion of Aur to cancer cells without significant involvement of normal cells. This issue was confirmed by different assays, including: MTT assay, flow cytometry, qPCR, and nuclei morphological observations. Furthermore, this study intensifies exploiting in silico approaches for adjusting drug delivery. The results of different assessments on designed peptides reveal an anticancer activity pattern rising from Aur toward Aurm, and R5- Aur, consecutively.

Conclusion

The designed structure of Aur shows improved anticancer activity through molecular changes which makes it suggestable for anticancer therapies.

Synthetic Star Nanoengineered Antimicrobial Polymers as Antibiofilm Agents: Bacterial Membrane Disruption and Cell Aggregation

Antimicrobial resistance has become a worldwide issue, with multiresistant bacterial strains emerging at an alarming rate. Multivalent antimicrobial polymer architectures such as bottle brush or star polymers have shown great potential, as they could lead to enhanced binding and interaction with the bacterial cell membrane. In this study, a library of amphiphilic star copolymers and their linear copolymer equivalents, based on acrylamide monomers, were synthesized via RAFT polymerization. Their monomer distribution and molecular weight were varied. Subsequently, their antimicrobial activity toward a Gram-negative bacterium (Pseudomonas aeruginosa PA14) and a Gram-positive bacterium (Staphylococcus aureus USA300) and their hemocompatibility were investigated. The statistical star copolymer, S-SP25, showed an improved antimicrobial activity compared to its linear equivalent againstP. aeruginosaPA14. The star architecture enhanced its antimicrobial activity, causing bacterial cell aggregation, as revealed via electron microscopy. However, it also induced increased red blood cell aggregation compared to its linear equivalents. Changing/shifting the position of the cationic block to the core of the structure prevents the cell aggregation effect while maintaining a potent antimicrobial activity for the smallest star copolymer. Finally, this compound showed antibiofilm properties against a robust in vitro biofilm model.

Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space

Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima. We present a multi-objective peptide design pipeline based on a discrete latent space and D-Wave quantum annealer with the aim of solving the local minima problem. To achieve multi-objective optimization, multiple peptide properties are encoded into a score using non-dominated sorting. Our pipeline is applied to design therapeutic peptides that are antimicrobial and non-hemolytic at the same time. From 200 000 peptides designed by our pipeline, four peptides proceeded to wet-lab validation. Three of them showed high anti-microbial activity, and two are non-hemolytic. Our results demonstrate how quantum-based optimizers can be taken advantage of in real-world medical studies.

Characterization and Engineering Studies of a New Endolysin from the Propionibacterium acnes Bacteriophage PAC1 for the Development of a Broad-Spectrum Artilysin with Altered Specificity

The emergence of multidrug-resistant (MDR) bacteria has risen rapidly, leading to a great threat to global public health. A promising solution to this problem is the exploitation of phage endolysins. In the present study, a putative N-acetylmuramoyl-L-alanine type-2 amidase (NALAA-2, EC 3.5.1.28) from Propionibacterium bacteriophage PAC1 was characterized. The enzyme (PaAmi1) was cloned into a T7 expression vector and expressed in E. coli BL21 cells. Kinetics analysis using turbidity reduction assays allowed the determination of the optimal conditions for lytic activity against a range of Gram-positive and negative human pathogens. The peptidoglycan degradation activity of PaAmi1 was confirmed using isolated peptidoglycan from P. acnes. The antibacterial activity of PaAmi1 was investigated using live P. acnes cells growing on agar plates. Two engineered variants of PaAmi1 were designed by fusion to its N-terminus two short antimicrobial peptides (AMPs). One AMP was selected by searching the genomes of Propionibacterium bacteriophages using bioinformatics tools, whereas the other AMP sequence was selected from the antimicrobial peptide databases. Both engineered variants exhibited improved lytic activity towards P. acnes and the enterococci species Enterococcus faecalis and Enterococcus faecium. The results of the present study suggest that PaAmi1 is a new antimicrobial agent and provide proof of concept that bacteriophage genomes are a rich source of AMP sequences that can be further exploited for designing novel or improved endolysins.

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

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

Broad-spectrum hybrid antimicrobial peptides derived from PMAP-23 with potential LPS binding ability

Antimicrobial peptides, as an integral part of the innate immune system, kill bacteria through a special mechanism of action, making them less susceptible to drug resistance. However, Lipopolysaccharide (LPS) as the permeation barrier on the bacterial membrane, inhibits the antibacterial activity of antimicrobial peptides and triggers the inflammatory response. GWKRKRFG is an LPS binding sequence with a β-boomerang motif that can be linked to antimicrobial peptides to enhance their LPS affinity and reduce the possibility of LPS-induced inflammatory responses. In this study, a series of hybrid peptides were designed by conjugating the reported LPS binding sequence to the C-/N-terminal sequences of the natural porcine antimicrobial peptide PMAP-23 to increase the LPS affinity of peptides. Among all the designed hybrid peptides, 4R-PP-G8 showed the best antibacterial activity, nonhemolytic activity, and excellent cell selectivity. The presence of LPS not only induced the secondary structure transformation of 4R-PP-G8 from a random structure to an α-helical structure but also reduced the antibacterial activity of 4R-PP-G8 in a dose-dependent manner, indicating the excellent binding ability of 4R-PP-G8 to LPS. The LPS/LTA binding assay further verified the interaction between the peptide and LPS. The membrane permeability test verified that 4R-PP-G8 possessed a strong capability to penetrate the bacterial membrane after interacting with LPS. More direct membrane disruption was observed under FE-SEM and TEM. In conclusion, we provided a simple and efficient method to improve the LPS binding ability of antimicrobial peptides and enhance their antimicrobial activity, resulting in the peptide 4R-PP-G8 with clinical application potential.

The biological role of charge distribution in linear antimicrobial peptides

INTRODUCTION

Antimicrobial peptides (AMP) have received particular attention due to their capacity to kill bacteria. Although much is known about them, peptides are currently being further researched. A large number of AMPs have been discovered, but only a few have been approved for topical use, due to their promiscuity and other challenges, which need to be overcome.

AREAS COVERED

AMPs are diverse in structure. Consequently, they have varied action mechanisms when targeting microorganisms or eukaryotic cells. Herein, the authors focus on linear peptides, particularly those that are alpha-helical structured, and examine how their charge distribution and hydrophobic amino acids could modulate their biological activity.

EXPERT OPINION

The world currently needs urgent solutions to the infective problems caused by resistant pathogens. In order to start the race for antimicrobial development from the charge distribution viewpoint, bioinformatic tools will be necessary. Currently, there is no software available that allows to discriminate charge distribution in AMPs and predicts the biological effects of this event. Furthermore, there is no software available that predicts the side-chain length of residues and its role in biological functions. More specialized software is necessary.

Rational design of stapled antimicrobial peptides

The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immunity, antimicrobial peptides have been recognized as the most promising candidates because they comprise diverse sequences and mechanisms of action and have a relatively low induction rate of resistance. However, because of their low chemical stability, susceptibility to proteases, and high hemolytic effect, their usage is subject to many restrictions. Chemical modifications such as D-amino acid substitution, cyclization, and unnatural amino acid modification have been used to improve the stability of antimicrobial peptides for decades. Among them, a side-chain covalent bridge modification, the so-called stapled peptide, has attracted much attention. The stapled side-chain bridge stabilizes the secondary structure, induces protease resistance, and increases cell penetration and biological activity. Recent progress in computer-aided drug design and artificial intelligence methods has also been used in the design of stapled antimicrobial peptides and has led to the successful discovery of many prospective peptides. This article reviews the possible structure-activity relationships of stapled antimicrobial peptides, the physicochemical properties that influence their activity (such as net charge, hydrophobicity, helicity, and dipole moment), and computer-aided methods of stapled peptide design. Antimicrobial peptides under clinical trial: Pexiganan (NCT01594762, 2012-05-07). Omiganan (NCT02576847, 2015-10-13).

Structural Analysis and Antimicrobial Mechanism of a Protein GBSPI-A from Ginkgo Biloba Seed

Ginkgo biloba seed has antimicrobial activity. In this study, ginkgo biloba seed protein was prepared, identified, and named GBSPI-A, finding its construction was similar to 11-S globulin. Then, the influence of GBSPI-A on the cell membrane and physiological metabolism of K. pneumoniae and S. aureus were investigated. The results showed that GBSPI-A (20 mg/mL) destroyed the cell membrane, causing leakage of intracellular material and inhibited bacterial growth with an inhibition rate of approximately 80%. In addition, the GBSPI-A (10 mg/mL) caused the decreasing activity of ATPase and respiratory rate, and the respiratory depression rate was 7.24%. Furthermore, the decreasing ATP synthesis and intracellular β-galactosidase activity led to an insufficient supply of physiological metabolic energy. Therefore, the results showed that GBSPI-A could be used as a natural bacteriostatic agent to replace related drugs and also provide a new insight into the application of GBSPI-A in food safety.

Hydrophobic diversification is the key to simultaneously increased antifungal activity and decreased cytotoxicity of two ab initio designed peptides

The fact that some antimicrobial peptides have been utilized clinically and as food preservatives stimulated the efforts in search of new candidates. In our previous studies, we succeeded in designing potent peptides against methicillin-resistant Staphylococcus aureus (MRSA), severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), and Ebola viruses based on the database filtering technology. The designed peptides were proved highly potent. However, this ab initio method has not been utilized to design antifungal peptides. This study report two novel antifungal peptides with 21 and 15 amino acids designed by more effectively extracting the most probable parameters from ∼1200 antifungal peptides in the antimicrobial peptide database (APD). Subsequent hydrophobic diversification led to two peptide variants with enhanced activity against four fungal strains but reduced cytotoxicity to four mammalian cell lines. DFTAFP-1A (KWSGAAAKKLKSLLSGLGKLL) and DFTAFP-2A (KWSGLLLKLGAASKL) retained activity against Zygosaccharomyces bailii at pH 5.6 and 6.3 or after autoclave. The peptides could permeabilize fungal membranes and adopted helical conformations in membrane mimetic micelles. Collectively, this study demonstrated not only the successful design of two novel antifungal peptides based on the APD database but also optimization of desired peptide properties. This improved database approach may be utilized to design useful peptides to combat other drug-resistant pathogens as well.

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure

MOTIVATION

Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance.

RESULTS

In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction.

AVAILABILITY AND IMPLEMENTATION

A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ornamental tobacco floral nectar is a rich source of antimicrobial peptides

Although floral nectar is a rich source of nutrients, it is rarely infected by microorganisms. Defense molecules such as proteins have been identified in this fluid, but defense peptides have been largely overlooked. Thus, the aim of this study was to perform an extensive peptidomic analysis of the ornamental tobacco floral nectar to seek peptides involved in nectar defense. Using LC-MS/MS, 793 peptides were sequenced and characterized. After extensive bioinformatics analysis, six peptides were selected for further characterization, synthesis, and evaluation of their antimicrobial properties against phytopathogenic fungi and bacteria. All six peptides had antimicrobial activity to some extent. However, the activity varied by peptide concentration and microorganism tested. An analysis of the action mechanism revealed damage in the cell membrane induced by peptides. The results show that floral nectar is rich in peptides and that, together with proteins and hydrogen peroxide, they contribute to plant defense against microorganisms during pollination.

Expanding the Landscape of Amino Acid-Rich Antimicrobial Peptides: Definition, Deployment in Nature, Implications for Peptide Design and Therapeutic Potential

Unlike the α-helical and β-sheet antimicrobial peptides (AMPs), our knowledge on amino acid-rich AMPs is limited. This article conducts a systematic study of rich AMPs (>25%) from different life kingdoms based on the Antimicrobial Peptide Database (APD) using the program R. Of 3425 peptides, 724 rich AMPs were identified. Rich AMPs are more common in animals and bacteria than in plants. In different animal classes, a unique set of rich AMPs is deployed. While histidine, proline, and arginine-rich AMPs are abundant in mammals, alanine, glycine, and leucine-rich AMPs are common in amphibians. Ten amino acids (Ala, Cys, Gly, His, Ile, Lys, Leu, Pro, Arg, and Val) are frequently observed in rich AMPs, seven (Asp, Glu, Phe, Ser, Thr, Trp, and Tyr) are occasionally observed, and three (Met, Asn, and Gln) were not yet found. Leucine is much more frequent in forming rich AMPs than either valine or isoleucine. To date, no natural AMPs are simultaneously rich in leucine and lysine, while proline, tryptophan, and cysteine-rich peptides can simultaneously be rich in arginine. These findings can be utilized to guide peptide design. Since multiple candidates are potent against antibiotic-resistant bacteria, rich AMPs stand out as promising future antibiotics.

Anticancer peptides mechanisms, simple and complex

Peptide therapy has started since 1920s with the advent of insulin application, and now it has emerged as a new approach in treatment of diseases including cancer. Using anti-cancer peptides (ACPs) is a promising way of cancer therapy as ACPs are continuing to be approved and arrived at major pharmaceutical markets. Traditional cancer treatments face different problems like intensive adverse effects to patient's body, cell resistance to conventional chemical drugs and in some worse cases the occurrence of cell multidrug resistance (MDR) of cancerous tissues against chemotherapy. On the other hand, there are some benefits conceived for peptides usage in treatment of diseases specifically cancer, as these compounds present favorable characteristics such as smaller size, high activity, low immunogenicity, good biocompatibility in vivo, convenient and rapid way of synthesis, amenable to sequence modification and revision and there is no limitation for the type of cargo they carry. It is possible to achieve an optimum molecular and functional structure of peptides based on previous experience and bank of peptide motif data which may result in novel peptide design. Bioactive peptides are able to form pores in cell membrane and induce necrosis or apoptosis of abnormal cells. Moreover, recent researches have focused on the tumor recognizing peptide motifs with the ability to permeate to cancerous cells with the aim of cancer treatment at earlier stages. In this strategy the most important factors for addressing cancer are choosing peptides with easy accessibility to tumor cell without cytotoxicity effect towards normal cells. The peptides must also meet acceptable pharmacokinetic requirements. In this review, the characteristics of peptides and cancer cells are discussed. The various mechanisms of peptides' action proposed against cancer cells make the next part of discussion. It will be followed by giving information on peptides application, various methods of peptide designing along with introducing various databases. Future aspects of peptides for employing in area of cancer treatment come as conclusion at the end.

Biofilms: Formation, Research Models, Potential Targets, and Methods for Prevention and Treatment

Due to the continuous rise in biofilm-related infections, biofilms seriously threaten human health. The formation of biofilms makes conventional antibiotics ineffective and dampens immune clearance. Therefore, it is important to understand the mechanisms of biofilm formation and develop novel strategies to treat biofilms more effectively. This review article begins with an introduction to biofilm formation in various clinical scenarios and their corresponding therapy. Established biofilm models used in research are then summarized. The potential targets which may assist in the development of new strategies for combating biofilms are further discussed. The novel technologies developed recently for the prevention and treatment of biofilms including antimicrobial surface coatings, physical removal of biofilms, development of new antimicrobial molecules, and delivery of antimicrobial agents are subsequently presented. Finally, directions for future studies are pointed out.

Regulation of LL-37 in Bone and Periodontium Regeneration

The goal of regenerative therapy is to restore the structure and function of the lost tissues in the fields of medicine and dentistry. However, there are some challenges in regeneration therapy such as the delivery of oxygen and nutrition, and the risk of infection in conditions such as periodontitis, osteomyelitis, etc. Leucine leucine-37 (LL-37) is a 37-residue, amphipathic, and helical peptide found only in humans and is expressed throughout the body. It has been shown to induce neovascularization and vascular endothelial growth factor (VEGF) expression. LL-37 also stimulates the migration and differentiation of mesenchymal stem cells (MSCs). Recent studies have shown that LL-37 plays an important role in the innate defense system through the elimination of pathogenic microbes and the modulation of the host immune response. LL-37 also manifests other functions such as promoting wound healing, angiogenesis, cell differentiation, and modulating apoptosis. This review summarizes the current studies on the structure, expression, and function of LL-37 and highlights the contributions of LL-37 to oral cavity, periodontium, and bone regeneration.

In silico discovery of biomarkers for the accurate and sensitive detection of Fusarium solani

Fusarium solani is worrisome because it severely threatens the agricultural productivity of certain crops such as tomatoes and peas, causing the general decline, wilting, and root necrosis. It has also been implicated in the infection of the human eye cornea. It is believed that early detection of the fungus could save these crops from the destructive activities of the fungus through early biocontrol measures. Therefore, the present work aimed to build a sensitive model of novel anti-Fusarium solani antimicrobial peptides (AMPs) against the fungal cutinase 1 (CUT1) protein for early, sensitive and accurate detection. Fusarium solani CUT1 receptor protein 2D secondary structure, model validation, and functional motifs were predicted. Subsequently, anti-Fusarium solani AMPs were retrieved, and the HMMER in silico algorithm was used to construct a model of the AMPs. After their structure predictions, the interaction analysis was analyzed for the Fusarium solani CUT1 protein and the generated AMPs. The putative anti-Fusarium solani AMPs bound the CUT1 protein very tightly, with OOB4 having the highest binding energy potential for HDock. The pyDockWeb generated high electrostatic, desolvation, and low van der Waals energies for all the AMPs against CUT1 protein, with OOB1 having the most significant interaction. The results suggested the utilization of AMPs for the timely intervention, control, and management of these crops, as mentioned earlier, to improve their agricultural productivity and reduce their economic loss and the use of HMMER for constructing models for disease detection.

Recent Advances in Multifunctional Antimicrobial Peptides as Immunomodulatory and Anticancer Therapy: Chromogranin A-Derived Peptides and Dermaseptins as Endogenous versus Exogenous Actors

Antimicrobial peptides (AMPs) are produced by all living organisms exhibiting antimicrobial activities and representing the first line of innate defense against pathogens. In this context, AMPs are suggested as an alternative to classical antibiotics. However, several researchers reported their involvement in different processes defining them as Multifunctional AMPs (MF-AMPs). Interestingly, these agents act as the endogenous responses of the human organism against several dangerous stimuli. Still, they are identified in other organisms and evaluated for their anticancer therapy. Chromogranin A (CgA) is a glyco-phosphoprotein discovered for the first time in the adrenal medulla but also produced in several cells. CgA can generate different derived AMPs influencing numerous physiological processes. Dermaseptins (DRSs) are a family of α-helical-shaped polycationic peptides isolated from the skin secretions of several leaf frogs from the Phyllomedusidae family. Several DRSs were identified as AMPs and, until now, more than 65 DRSs have been classified. Recently, these exogenous molecules were characterized for their anticancer activity. In this review, we summarize the role of these two classes of MF-AMPs as an example of endogenous molecules for CgA-derived peptides, able to modulate inflammation but also as exogenous molecules for DRSs, exerting anticancer activities.

Knowledgebase of potential multifaceted solutions to antimicrobial resistance

Antimicrobial resistance (AMR), a top threat to global health, challenges preventive and treatment strategies of infections. AMR strains of microbial pathogens arise through multiple mechanisms. The underlying "antibiotic resistance genes" (ARGs) spread through various species by lateral gene transfer thereby causing global dissemination. Human methods also augment this process through inappropriate use, non-compliance to treatment schedule, and environmental waste. Worldwide significant efforts are being invested to discover novel therapeutic solutions for tackling resistant pathogens. Diverse therapeutic strategies have evolved over recent years. In this work we have developed a comprehensive knowledgebase by collecting alternative antimicrobial therapeutic strategies from literature data. Therapeutic strategies against bacteria, virus, fungus and parasites were extracted from PubMed literature using text mining. We have used a subjective (sentimental) approach for data mining new strategies, resulting in broad coverage of novel entities and subsequently add objective data like entity name (including IUPAC), potency, and safety information. The extracted data was organized in a freely accessible web platform, KOMBAT. The KOMBAT comprises 1104 Chemical compounds, 220 of newly identified antimicrobial peptides, 42 bacteriophages, 242 phytochemicals, 106 nanocomposites, and 94 novel entities for phototherapy. Entities tested and evaluated on AMR pathogens are included. We envision that this database will be useful for developing future therapeutics against AMR pathogens. The database can be accessed through http://kombat.igib.res.in/.

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies.