CAMPR4: a database of natural and synthetic antimicrobial peptides

The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides

Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.

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.

The role and future prospects of artificial intelligence algorithms in peptide drug development

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Genomic and physiological characterization of Kitasatospora sp. nov., an actinobacterium with potential for biotechnological application isolated from Cerrado soil

An Actinobacteria - Kitasatospora sp. K002 - was isolated from the soil of Cerrado, a savanna-like Brazilian biome. Herein, we conducted a phylogenetic, phenotypic and physiological characterization, revealing its potential for biotechnological applications. Kitasatospora sp. K002 is an aerobic, non-motile, Gram-positive bacteria that forms grayish-white mycelium on solid cultures and submerged spores with vegetative mycelia on liquid cultures. The strain showed antibacterial activity against Bacillus subtilis, Pseudomonas aeruginosa and Escherichia coli. Genomic analysis indicated that Kitasatospora xanthocidica JCM 4862 is the closest strain to K002, with a dDDH of 32.8-37.8% and an ANI of 86.86% and the pangenome investigations identified a high number of rare genes. A total of 60 gene clusters of 22 different types were detected by AntiSMASH, and 22 gene clusters showed low similarity (< 10%) with known compounds, which suggests the potential production of novel bioactive compounds. In addition, phylogenetic analysis and morphophysiological characterization clearly distinguished Kitasatospora sp. K002 from other related species. Therefore, we propose that Kitasatospora sp. K002 should be recognized as a new species of the genus Kitasatospora - Kitasatospora brasiliensis sp. nov. (type strains = K002).

THPdb2: compilation of FDA approved therapeutic peptides and proteins

During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks

Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.

iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides

With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

sRAGE-binding and antimicrobial bioactivities of soy and pea protein after heating and in vitro infant digestion

During infant formula production, proteins are always heated, potentially affecting their digestibility and the bioactivities of resulting peptides. Although plant proteins are a promising dairy alternative for infant formula, they remain understudied, necessitating further investigations. Therefore, this research aimed to fill this gap by assessing the impact of different heating modes on soy protein (SP) and pea protein (PP), focusing on glycation levels, peptide formation during in vitro infant digestion, and immune protection potential (sRAGE-binding and antimicrobial activities) of the resulting peptides. Consequently, dry heating led to increased glycation and glycated peptide production, particularly with higher glycation in PP than SP. Moreover, PP exhibited an overall stronger sRAGE-binding capacity than SP, regardless of heating and digestion conditions. Regarding antimicrobial activity, both SP and PP-derived peptides displayed reduced effectiveness against Enterobacter cloacae after dry heating. Additionally, Staphylococcus epidermidis was differently inhibited, where PP-derived peptides showed inherent inhibition. The primary determinant of sRAGE-binding and antimicrobial potential in digestion-derived peptides was the protein source. Subsequent bioinformatics analysis predicted 519 and 133 potential antimicrobial peptides in SP and PP, respectively. This study emphasises the importance of protein source for infant formula to ensure infant health.

Nanofiber Peptides for Bacterial Trapping: A Novel Approach to Antibiotic Alternatives in Wound Infections

The pervasive employment of antibiotics has engendered the advent of drug-resistant bacteria, imperiling the well-being and health of both humans and animals. Infections precipitated by such multi-resistant bacteria, especially those induced by methicillin-resistant Staphylococcus aureus (MRSA), pervade hospital settings, constituting a grave menace to patient vitality. Antimicrobial peptides (AMPs) have garnered considerable attention as a potent countermeasure against multidrug resistant bacteria. In preceding research endeavors, an insect-derived antimicrobial peptide is identified that, while possessing antimicrobial attributes, manifested suboptimal efficacy against drug-resistant Gram-positive bacteria. To ameliorate this issue, this work enhances the antimicrobial capabilities of the initial β-hairpin AMPs by substituting the structural sequence of the original AMPs with variant lengths of hydrophobic amino acid-hydrophilic amino acid repeat units. Throughout this endeavor, this work has identified a number of peptides that possess highly effective antibacterial characteristics against a wide range of bacteria. Additionally, some of these peptides have the ability to self-assemble into nanofibers, which then build networks in a distinctive manner to capture bacteria. Consequently, they represent prospective antibiotic alternatives for addressing wound infections engendered by drug-resistant bacteria.

Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning

The emergence of antibiotic-resistant bacteria poses a huge threat to the treatment of infections. Antimicrobial peptides are a class of short peptides that widely exist in organisms and are considered as potential substitutes for traditional antibiotics. Here, we use metagenomics combined with machine learning to find antimicrobial peptides from environmental metagenomes and successfully obtained 16,044,909 predicted AMPs. We compared the abundance of potential antimicrobial peptides in natural environments and engineered environments, and found that engineered environments also have great potential. Further, we chose sludge as a typical engineered environmental sample, and tried to mine antimicrobial peptides from it. Through metaproteome analysis and correlation analysis, we mined 27 candidate AMPs from sludge. We successfully synthesized 25 peptides by chemical synthesis, and experimentally verified that 21 peptides had antibacterial activity against the 4 strains tested. Our work highlights the potential for mining new antimicrobial peptides from engineered environments and demonstrates the effectiveness of mining antimicrobial peptides from sludge.

Heterologous Production of Antimicrobial Peptides: Notes to Consider

Heavy and irresponsible use of antibiotics in the last century has put selection pressure on the microbes to evolve even faster and develop more resilient strains. In the confrontation with such sometimes called "superbugs", the search for new sources of biochemical antibiotics seems to have reached the limit. In the last two decades, bioactive antimicrobial peptides (AMPs), which are polypeptide chains with less than 100 amino acids, have attracted the attention of many in the control of microbial pathogens, more than the other types of antibiotics. AMPs are groups of components involved in the immune response of many living organisms, and have come to light as new frontiers in fighting with microbes. AMPs are generally produced in minute amounts within organisms; therefore, to address the market, they have to be either produced on a large scale through recombinant DNA technology or to be synthesized via chemical methods. Here, heterologous expression of AMPs within bacterial, fungal, yeast, plants, and insect cells, and points that need to be considered towards their industrialization will be reviewed.

Transcriptome-Wide Identification and Expression Analysis of Genes Encoding Defense-Related Peptides of Filipendula ulmaria in Response to Bipolaris sorokiniana Infection

Peptides play an essential role in plant development and immunity. Filipendula ulmaria, belonging to the Rosaceae family, is a medicinal plant which exhibits valuable pharmacological properties. F. ulmaria extracts in vitro inhibit the growth of a variety of plant and human pathogens. The role of peptides in defense against pathogens in F. ulmaria remains unknown. The objective of this study was to explore the repertoire of antimicrobial (AMPs) and defense-related signaling peptide genes expressed by F. ulmaria in response to infection with Bipolaris sorokiniana using RNA-seq. Transcriptomes of healthy and infected plants at two time points were sequenced on the Illumina HiSeq500 platform and de novo assembled. A total of 84 peptide genes encoding novel putative AMPs and signaling peptides were predicted in F. ulmaria transcriptomes. They belong to known, as well as new, peptide families. Transcriptional profiling in response to infection disclosed complex expression patterns of peptide genes and identified both up- and down-regulated genes in each family. Among the differentially expressed genes, the vast majority were down-regulated, suggesting suppression of the immune response by the fungus. The expression of 13 peptide genes was up-regulated, indicating their possible involvement in triggering defense response. After functional studies, the encoded peptides can be used in the development of novel biofungicides and resistance inducers.

The global burden of antimicrobial resistance - urinary tract infections

Antimicrobial resistance (AMR) has emerged as a significant global healthcare problem. Antibiotic use has accelerated the physiologic process of AMR, particularly in Gram-negative pathogens. Urinary tract infections (UTIs) are predominantly of a Gram-negative nature. Uropathogens are evolutionarily highly adapted and selected strains with specific virulence factors, suggesting common mechanisms in how bacterial cells acquire virulence and AMR factors. The simultaneous increase in resistance and virulence is a complex and context-dependent phenomenon. Among known AMR mechanisms, the plenitude of different β-lactamases is especially prominent. The risk for AMR in UTIs varies in different patient populations. A history of antibiotic consumption and the physiology of urinary flow are major factors that shape AMR prevalence. The urinary tract is in close crosstalk with the microbiome of other compartments, including the gut and genital tracts. In addition, pharmacokinetic properties and the physiochemical composition of urinary compartments can contribute to the emergence of AMR. Alternatives to antibiotic treatment and a broader approach to address bacterial infections are needed. Among the various alternatives studied, antimicrobial peptides and bacteriophage treatment appear to be highly promising approaches. We herein summarize the present knowledge of clinical and microbiological AMR in UTIs and discuss innovative approaches, namely new risk prediction tools and the use of non-antibiotic approaches to defend against uropathogenic microbes.

ESKtides: a comprehensive database and mining method for ESKAPE phage-derived antimicrobial peptides

'Superbugs' have received increasing attention from researchers, such as ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.), which directly led to about 1 270 000 death cases in 2019. Recently, phage peptidoglycan hydrolases (PGHs)-derived antimicrobial peptides were proposed as new antibacterial agents against multidrug-resistant bacteria. However, there is still a lack of methods for mining antimicrobial peptides based on phages or phage PGHs. Here, by using a collection of 6809 genomes of ESKAPE isolates and corresponding phages in public databases, based on a unified annotation process of all the genomes, PGHs were systematically identified, from which peptides were mined. As a result, a total of 12 067 248 peptides with high antibacterial activities were respectively determined. A user-friendly tool was developed to predict the phage PGHs-derived antimicrobial peptides from customized genomes, which also allows the calculation of peptide phylogeny, physicochemical properties, and secondary structure. Finally, a user-friendly and intuitive database, ESKtides (http://www.phageonehealth.cn:9000/ESKtides), was designed for data browsing, searching and downloading, which provides a rich peptide library based on ESKAPE prophages and phages. Database URL:  10.1093/database/baae022.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

19F solid-state NMR approaches to probe antimicrobial peptide interactions with membranes in whole cells

To address the global problem of bacterial antibiotic resistance, antimicrobial peptides (AMPs) are considered promising therapeutic candidates due to their broad-spectrum and membrane-lytic activity. As preferential interactions with bacteria are crucial, it is equally important to investigate and understand their impact on eukaryotic cells. In this study, we employed 19F solid-state nuclear magnetic resonance (ssNMR) as a novel approach to examine the interaction of AMPs with whole red blood cells (RBCs). We used RBC ghosts (devoid of hemoglobin) and developed a protocol to label their lipid membranes with palmitic acid (PA) monofluorinated at carbon positions 4, 8, or 14 on the acyl chain, allowing us to probe different locations in model and intact RBC ghost membranes. Our work revealed that changes in the 19F chemical shift anisotropy, monitored through a CF bond order parameter (SCF), can provide insights into lipid bilayer dynamics. This information was also obtained using magic-angle spinning 19F ssNMR spectra with and without 1H decoupling, by studying alterations in the second spectral moment (M2) as well as the 19F isotropic chemical shift, linewidth, T1, and T2 relaxation times. The appearance of an additional isotropic peak with a smaller chemical shift anisotropy, a narrower linewidth, and a shorter T1, induced by the AMP caerin 1.1, supports the presence of high-curvature regions in RBCs indicative of pore formation, analogous to its antimicrobial mechanism. In summary, the straightforward incorporation of monofluorinated FAs and rapid signal acquisition offer promising avenues for the study of whole cells using 19F ssNMR.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

Developing Lipase Inhibitor as a Novel Approach to Address the Rice Bran Rancidity Issue─A Critical Review

Rice bran is a valuable byproduct from the food processing industry, which contains abundant protein, essential unsaturated fatty acids, and numerous bioactive compounds. However, its susceptibility to rancidity greatly restricts its wide utilization. Many strategies have been proposed to delay the rancidity of rice bran, but most of them have their respective limitations. Here, we proposed that developing rice ban lipase peptide inhibitors represents an alternative and promising prescription for impeding the rancidity of rice bran, in contrast to the conventional stabilization approaches for rice bran. For this reason, the rancidity mechanisms of rice bran and the research progress of rice bran lipases were discussed. In addition, the feasibility of utilizing in silico screening and phage display, two state-of-the-art technologies, in the design of the related peptide inhibitors was also highlighted. This knowledge is expected to provide a theoretical basis for opening a new avenue for stabilizing rice bran.

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.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.

Recent Advances on Antimicrobial Peptides from Milk: Molecular Properties, Mechanisms, and Applications

Traditional antibiotics are facing a tremendous challenge due to increased antimicrobial resistance; hence, there is an urgent need to find novel antibiotic alternatives. Milk protein-derived antimicrobial peptides (AMPs) are currently attracting substantial attention considering that they showcase an extensive spectrum of antimicrobial activities, with slower development of antimicrobial resistance and safety of raw materials. This review summarizes the molecular properties, and activity mechanisms and highlights the applications and limitations of AMPs derived from milk proteins comprehensively. Also the analytical technologies, especially bioinformatics methodologies, applied in the process of screening, identification, and mechanism illustration of AMPs were underlined. This review will give some ideas for further research and broadening of the applications of milk protein-derived AMPs in the food field.

Advances in separation and identification of biologically important milk proteins and peptides

Milk is a rich source of biologically important proteins and peptides. In addition, milk contains a variety of extracellular vesicles (EVs), including exosomes, that carry their own proteome cargo. EVs are essential for cell-cell communication and modulation of biological processes. They act as nature carriers of bioactive proteins/peptides in targeted delivery during various physiological and pathological conditions. Identification of the proteins and protein-derived peptides in milk and EVs and recognition of their biological activities and functions had a tremendous impact on food industry, medicine research, and clinical applications. Advanced separation methods, mass spectrometry (MS)-based proteomic approaches and innovative biostatistical procedures allowed for characterization of milk protein isoforms, genetic/splice variants, posttranslational modifications and their key roles, and contributed to novel discoveries. This review article discusses recently published developments in separation and identification of bioactive proteins/peptides from milk and milk EVs, including MS-based proteomic approaches.

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.

Designing Cell Delivery Peptides and SARS-CoV-2-Targeting Small Interfering RNAs: A Comprehensive Bioinformatics Study with Generative Adversarial Network-Based Peptide Design and In Vitro Assays

Nucleic acid technologies with designed intracellular delivery systems are some of the most promising therapies of the future. Small interfering (si)RNAs inhibit gene expression and protein synthesis and may complement current vaccines with faster design and production. Although successful delivery remains an issue, delivery peptides may help to fill this gap. Here, we address this issue by applying bioinformatic approaches to design new putative cell delivery peptides and siRNAs for COVID-19 variants and other related viral diseases. Of the 29,880 RNA sequences analyzed, 62 were identified in silico as able to target the virus mRNA sequence, and from the 9,984 peptide sequences analyzed, 10 were selected as delivery peptides. From the latter, we further performed in vitro studies of the two best-ranked peptides and compared them with the broadly used TAT delivery peptide. One of them, seq5, displayed better internalization results with about double intensity signal compared to TAT after a 1 h incubation time in GFP-HeLa cells. This peptide has, thus, the features of a delivery peptide and could be used for cargo intracellular delivery.

Screening and investigation of a short antimicrobial peptide: AVGAV

Bacterial resistance to various drugs is a major problem concerning the field of antibacterial agents. Fortunately, peptides with antibacterial activity can alleviate this problem. In this study, a short peptide (AVGAV) with excellent antibacterial activity was successfully screened from a peptide library by a self-made membrane chromatographic packing. The AVGAV peptide exhibits good biocompatibility and is non-toxic and non-irritating, which ensures that it presents safe antibacterial effects. AVGAV promoted wound healing in a mouse wound bacterial infection model. Most importantly, as a synthetic antimicrobial peptide, AVGAV can alleviate the problem of bacterial resistance, thus improving its application potential. This study provides a solution to the existing and potential problem of bacterial resistance.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/ .

Topoisomeric Membrane-Active Peptides: A Review of the Last Two Decades

In recent decades, bioactive peptides have been gaining recognition in various biomedical areas, such as intracellular drug delivery (cell-penetrating peptides, CPPs) or anti-infective action (antimicrobial peptides, AMPs), closely associated to their distinct mode of interaction with biological membranes. Exploiting the interaction of membrane-active peptides with diverse targets (healthy, tumoral, bacterial or parasitic cell membranes) is opening encouraging prospects for peptides in therapeutics. However, ordinary peptides formed by L-amino acids are easily decomposed by proteases in biological fluids. One way to sidestep this limitation is to use topoisomers, namely versions of the peptide made up of D-amino acids in either canonic (enantio) or inverted (retroenantio) sequence. Rearranging peptide sequences in this fashion provides a certain degree of native structure mimicry that, in appropriate contexts, may deliver desirable biological activity while avoiding protease degradation. In this review, we will focus on recent accounts of membrane-active topoisomeric peptides with therapeutic applications as CPP drug delivery vectors, or as antimicrobial and anticancer candidates. We will also discuss the most common modes of interaction of these peptides with their membrane targets.

The NIH Comparative Genomics Resource: addressing the promises and challenges of comparative genomics on human health

Comparative genomics is the comparison of genetic information within and across organisms to understand the evolution, structure, and function of genes, proteins, and non-coding regions (Sivashankari and Shanmughavel, Bioinformation 1:376-8, 2007). Advances in sequencing technology and assembly algorithms have resulted in the ability to sequence large genomes and provided a wealth of data that are being used in comparative genomic analyses. Comparative analysis can be leveraged to systematically explore and evaluate the biological relationships and evolution between species, aid in understanding the structure and function of genes, and gain a better understanding of disease and potential drug targets. As our knowledge of genetics expands, comparative genomics can help identify emerging model organisms among a broader span of the tree of life, positively impacting human health. This impact includes, but is not limited to, zoonotic disease research, therapeutics development, microbiome research, xenotransplantation, oncology, and toxicology. Despite advancements in comparative genomics, new challenges have arisen around the quantity, quality assurance, annotation, and interoperability of genomic data and metadata. New tools and approaches are required to meet these challenges and fulfill the needs of researchers. This paper focuses on how the National Institutes of Health (NIH) Comparative Genomics Resource (CGR) can address both the opportunities for comparative genomics to further impact human health and confront an increasingly complex set of challenges facing researchers.

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.

iASMP: An interpretable in-silico predictive tool focusing on species-specific antimicrobial peptides

Antimicrobial peptides (AMPs), a crucial part of the innate immune system, have been exploited as promising candidates for antibacterial agents. Many researchers have been devoting their efforts to develop novel AMPs in recent decades. In this term, many computational approaches have been developed to identify potential AMPs accurately. However, finding peptides specific to a particular bacterial species is challenging. Streptococcus mutans is a pathogen with an apparent cariogenic effect, and it is of great significance to study AMP that inhibit S. mutans for the prevention and treatment of caries. In this study, we proposed a sequence-based machine learning model, namely iASMP, to exactly identify potential anti-S. mutans peptides (ASMPs). After collecting ASMPs, the performances of models were compared by utilizing multiple feature descriptors and different classification algorithms. Among the baseline predictors, the model integrating the extra trees (ET) algorithm and the hybrid features exhibited optimal results. The feature selection method was utilized to remove redundant feature information to improve the model performance further. Finally, the proposed model achieved the maximum accuracy (ACC) of 0.962 on the training dataset and performed on the testing dataset with an ACC of 0.750. The results demonstrated that iASMP had an excellent predictive performance and was suitable for identifying potential ASMP. Furthermore, we also visualized the selected features and rationally explained the impact of individual features on the model output.

Can BioSAXS detect ultrastructural changes of antifungal compounds in Candida albicans?-an exploratory study

The opportunistic yeast Candida albicans is the most common cause of candidiasis. With only four classes of antifungal drugs on the market, resistance is becoming a problem in the treatment of fungal infections, especially in immunocompromised patients. The development of novel antifungal drugs with different modes of action is urgent. In 2016, we developed a groundbreaking new medium-throughput method to distinguish the effects of antibacterial agents. Using small-angle X-ray scattering for biological samples (BioSAXS), it is now possible to screen hundreds of new antibacterial compounds and select those with the highest probability for a novel mode of action. However, yeast (eukaryotic) cells are highly structured compared to bacteria. The fundamental question to answer was if the ultrastructural changes induced by the action of an antifungal drug can be detected even when most structures in the cell stay unchanged. In this exploratory work, BioSAXS was used to measure the ultrastructural changes of C. albicans that were directly or indirectly induced by antifungal compounds. For this, the well-characterized antifungal drug Flucytosine was used. BioSAXS measurements were performed on the synchrotron P12 BioSAXS beamline, EMBL (DESY, Hamburg) on treated and untreated yeast C. albicans. BioSAXS curves were analysed using principal component analysis (PCA). The PCA showed that Flucytosine-treated and untreated yeast were separated. Based on that success further measurements were performed on five antifungal peptides {1. Cecropin A-melittin hybrid [CA (1-7) M (2-9)], KWKLFKKIGAVLKVL; 2. Lasioglossin LL-III, VNWKKILGKIIKVVK; 3. Mastoparan M, INLKAIAALAKKLL; 4. Bmkn2, FIGAIARLLSKIFGKR; and 5. optP7, KRRVRWIIW}. The ultrastructural changes of C. albicans indicate that the peptides may have different modes of action compared to Flucytosine as well as to each other, except for the Cecropin A-melittin hybrid [CA (1-7) M (2-9)] and optP7, showing very similar effects on C. albicans. This very first study demonstrates that BioSAXS shows promise to be used for antifungal drug development. However, this first study has limitations and further experiments are necessary to establish this application.

Deep learning tools to accelerate antibiotic discovery

INTRODUCTION

As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity.

AREAS COVERED

This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics.

EXPERT OPINION

Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.

Peptidomics Analysis Reveals the Buccal Gland of Jawless Vertebrate Lamprey as a Source of Multiple Bioactive Peptides

Various proteins with antibacterial, anticoagulant, and anti-inflammatory properties have been identified in the buccal glands of jawless blood-sucking vertebrate lampreys. However, studies on endogenous peptides in the buccal gland of lampreys are limited. In this study, 4528 endogenous peptides were identified from 1224 precursor proteins using peptidomics and screened for bioactivity in the buccal glands of the lamprey, Lethenteron camtschaticum. We synthesized four candidate bioactive peptides (VSLNLPYSVVRGEQFVVQA, DIPVPEVPILE, VVQLPPVVLGTFG, and VPPPPLVLPPASVK), calculated their secondary structures, and validated their bioactivity. The results showed that the peptide VSLNLPYSVVRGEQFVVQA possessed anti-inflammatory activity, which significantly increased the expression of anti-inflammatory factors and decreased the expression of inflammatory factors in THP-1 cells. The peptide VVQLPPVVLGTFG showed antibacterial activity against some gram-positive bacteria. The peptide VSLNLPYSVVRGEQFVQA possessed good ACE inhibitory activity at low concentrations, but no dose-related correlation was observed. Our study revealed that the buccal glands of the jawless vertebrate lamprey are a source of multiple bioactive peptides, which will provide new insights into the blood-sucking mechanism of lamprey.

Isolation and Optimization of a Broad-Spectrum Synthetic Antimicrobial Peptide, Ap920-WI, from Arthrobacter sp. H5 for the Biological Control of Plant Diseases

Antimicrobial peptides (AMPs) are naturally occurring molecules found in various organisms that can help to defend against invading microorganisms and reduce the likelihood of drug resistance development. This study focused on the isolation of new AMPs from the genome library of a Gram-positive bacterium called Arthrobacter sp. H5. To achieve this, we used the Bacillus subtilis expression system and employed bioinformatics techniques to optimize and modify the peptides, resulting in the development of a new synthetic antimicrobial peptide (SAMP). Ap920 is expected to be a new antimicrobial peptide with a high positive charge (+12.5). Through optimization, a new synthetic antimicrobial peptide, Ap920-WI, containing only 15 amino acids, was created. Thereafter, the antimicrobial and antifungal activities of Ap920-WI were determined using minimum inhibitory concentration (MIC) and the concentration for 50% of maximal effect (EC₅₀). The Ap920-WI peptide was observed to target the outer membrane of fungal hyphae, leading to inhibition of growth in Rhizoctonia Solani, Sclerotinia sclerotiorum, and Botrytis cinerea. In plants, Ap920-WI showed significant antifungal activity and inhibited the infestation of S. sclerotiorum on rape leaves. Importantly, Ap920-WI was found to be safe for mammalian cells since it did not show any hemolytic activity against sheep red blood cells. Overall, the study found that the new synthetic antimicrobial peptide Ap920-WI exhibits broad-spectrum activity against microorganisms and may offer a new solution for controlling plant diseases, as well as hold potential for drug development.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Design and Evaluation of a Novel Anti-microbial Peptide from Cathelicidin-2: Selectively Active Against Acinetobacter baumannii

Background

Infections caused by pathogenic microorganisms have increased the need for hospital care and have thus represented a public health problem and a significant financial burden. Classical treatments consisting of traditional antibiotics face several challenges today. Anti-microbial peptides (AMPs) are a conserved characteristic of the innate immune response among different animal species to defend against pathogenic microorganisms.

Objectives

In this study, a new peptide sequence (mCHTL131-140) was designed using the in silico approach.

Methods

Cathelicidin-2 (UniprotID: Q2IAL7) was used as a potential antimicrobial protein, and a novel 10 - 12 amino acids sequence AMP was designed using bioinformatics tools and the AMP databases. Then, the anti-bacterial, anti-biofilm, and anti-fungal properties of the peptide, as well as its hemolytic activity and cytotoxicity towards human fibroblast (HDF) cells, were investigated in vitro.

Results

Online bioinformatics tools indicated that the peptide sequence could have anti-bacterial, anti-viral, anti-fungal, and anti-biofilm properties with little hemolytic properties. The experimental tests confirmed that mCHTL131-140 exhibited the best anti-bacterial properties against Acinetobacter baumannii and had fair anti-fungal properties. Besides, it did not cause red blood cell lysis and showed no cytotoxicity towards HDF cells.

Conclusions

In general, the designed peptide can be considered a promising AMP to control hospital-acquired infections by A. baumannii.