Ab initio design of potent anti-MRSA peptides based on database filtering technology

Molecular Dynamics Simulations Help Determine the Molecular Mechanisms of Lasioglossin-III and Its Variant Peptides' Membrane Interfacial Interactions

Lasioglossin-III (LL-III) is a potent broad-spectrum antimicrobial peptide used in diverse antimicrobial applications. In this work, coarse-grained and all-atom molecular dynamics simulation strategies were used in tandem to interpret the molecular mechanisms involved in the interfacial dynamics of LL-III and its recombinant variants during interactions with diverse cell membrane systems. Our results indicate that the membrane charges act as the driving force for initiating the membrane-peptide interactions, while the hydrophobic or van der Waals forces help to reinforce the membrane-peptide bindings. The optimized charge-hydrophobicity ratio of the LL-III peptides helps ensure their high specificity toward bacterial membranes compared to mammalian membrane systems, which also helps explain our experimental observations. Overall, we hope that our work gives new insight into the antimicrobial action of LL-III peptides and that the adopted simulation strategy will help other scientists and engineers extract maximal information from complex molecular simulations using minimal computational power.

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.

Unlocking Antibacterial Potential: Key-Site-Based Regulation of Antibacterial Spectrum of Peptides

In the pursuit of combating multidrug-resistant bacteria, antimicrobial peptides (AMPs) have emerged as promising agents; however, their application in clinical settings still presents challenges. Specifically, the exploration of crucial structural parameters that influence the antibacterial spectrum of AMPs and the subsequent development of tailored variants with either broad- or narrow-spectrum characteristics to address diverse clinical therapeutic needs has been overlooked. This study focused on investigating the effects of amino acid sites and hydrophobicity on the peptide's antibacterial spectrum through Ala scanning and fixed-point hydrophobic amino acid substitution techniques. The findings revealed that specific amino acid sites played a pivotal role in determining the antibacterial spectrum of AMPs and confirmed that broadening the spectrum could be achieved only by increasing hydrophobicity at certain positions. In conclusion, this research provided a theoretical basis for future precise regulation of an antimicrobial peptide's spectrum by emphasizing the intricate balance between amino acid sites and hydrophobicity.

Specifically targeted antimicrobial peptides synergize with bacterial-entrapping peptide against systemic MRSA infections

INTRODUCTION

The design of precision antimicrobials aims to personalize the treatment of drug-resistant bacterial infections and avoid host microbiota dysbiosis.

OBJECTIVES

This study aimed to propose an efficient de novo design strategy to obtain specifically targeted antimicrobial peptides (STAMPs) against methicillin-resistant Staphylococcus aureus (MRSA).

METHODS

We evaluated three strategies designed to increase the selectivity of antimicrobial peptides (AMPs) for MRSA and mainly adopted an advanced hybrid peptide strategy. First, we proposed a traversal design to generate sequences, and constructed machine learning models to predict the anti-S. aureus activity levels of unknown peptides. Subsequently, six peptides were predicted to have high activity, among which, a broad-spectrum AMP (P18) was selected. Finally, the two targeting peptides from phage display libraries or lysostaphin were used to confer specific anti-S. aureus activity to P18. STAMPs were further screened out from hybrid peptides based on their in vitro and in vivo activities.

RESULTS

An advanced hybrid peptide strategy can enhance the specific and targeted properties of broad-spectrum AMPs. Among 25 assessed peptides, 10 passed in vitro tests, and two peptides containing one bacterial-entrapping peptide (BEP) and one STAMP passed an in vivo test. The lead STAMP (P18E6) disrupted MRSA cell walls and membranes, eliminated established biofilms, and exhibited desirable biocompatibility, systemic distribution and efficacy, and immunomodulatory activity in vivo. Furthermore, a bacterial-entrapping peptide (BEP, SP5) modified from P18, self-assembled into nanonetworks and rapidly entrapped MRSA. SP5 synergized with P18E6 to enhance antibacterial activity in vitro and reduced systemic MRSA infections.

CONCLUSIONS

This strategy may aid in the design of STAMPs against drug-resistant strains, and BEPs can serve as powerful STAMP adjuvants.

Recent Developments and Future Perspectives of Purine Derivatives as a Promising Scaffold in Drug Discovery

Numerous purine-containing compounds have undergone extensive investigation for their medical efficacy across various diseases. The swift progress in purine-based medicinal chemistry has brought to light the therapeutic capabilities of purine-derived compounds in addressing challenging medical conditions. Defined by a heterocyclic ring comprising a pyrimidine ring linked with an imidazole ring, purine exhibits a diverse array of therapeutic attributes. This review systematically addresses the multifaceted potential of purine derivatives in combating various diseases, including their roles as anticancer agents, antiviral compounds (anti-herpes, anti-HIV, and anti-influenzae), autoimmune and anti-inflammatory agents, antihyperuricemic and anti-gout solutions, antimicrobial agents, antitubercular compounds, anti-leishmanial agents, and anticonvulsants. Emphasis is placed on the remarkable progress made in developing purine-based compounds, elucidating their significant target sites. The article provides a comprehensive exploration of developments in both natural and synthetic purines, offering insights into their role in managing a diverse range of illnesses. Additionally, the discussion delves into the structure-activity relationships and biological activities of the most promising purine molecules. The intriguing capabilities revealed by these purine-based scaffolds unequivocally position them at the forefront of drug candidate development. As such, this review holds potential significance for researchers actively involved in synthesizing purine-based drug candidates, providing a roadmap for the continued advancement of this promising field.

Conjugation of antimicrobial peptides to enhance therapeutic efficacy

The growing prevalence of antimicrobial resistance (AMR) has brought with it a continual increase in the numbers of deaths from multidrug-resistant (MDR) infections. Since the current arsenal of antibiotics has become increasingly ineffective, there exists an urgent need for discovery and development of novel antimicrobials. Antimicrobial peptides (AMPs) are considered to be a promising class of molecules due to their broad-spectrum activities and low resistance rates compared with other types of antibiotics. Since AMPs also often play major roles in elevating the host immune response, the molecules may also be called "host defense peptides." Despite the great promise of AMPs, the majority remain unsuitable for clinical use due to issues of structural instability, degradation by proteases, and/or toxicity to host cells. Moreover, AMP activities in vivo can be influenced by many factors, such as interaction with blood and serum biomolecules, physiological salt concentrations or different pH values. To overcome these limitations, structural modifications can be made to the AMP. Among several modifications, physical and chemical conjugation of AMP to other biomolecules is widely considered an effective strategy. In this review, we discuss structural modification strategies related to conjugation of AMPs and their possible effects on mode of action. The conjugation of fatty acids, glycans, antibiotics, photosensitizers, polymers, nucleic acids, nanoparticles, and immobilization to biomaterials are highlighted.

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.

Gut-targeted nanoparticles deliver specifically targeted antimicrobial peptides against Clostridium perfringens infections

Specifically targeted antimicrobial peptides (STAMPs) are novel alternatives to antibiotics, whereas the development of STAMPs for colonic infections is hindered by limited de novo design efficiency and colonic bioavailability. In this study, we report an efficient de novo STAMP design strategy that combines a traversal design, machine learning model, and phage display technology to identify STAMPs against Clostridium perfringens. STAMPs could physically damage C. perfringens, eliminate biofilms, and self-assemble into nanoparticles to entrap pathogens. Further, a gut-targeted engineering particle vaccine (EPV) was used for STAMPs delivery. In vivo studies showed that both STAMP and EPV@STAMP effectively limited C. perfringens infections and then reduced inflammatory response. Notably, EPV@STAMP exhibited stronger protection against colonic infections than STAMPs alone. Moreover, 16S ribosomal RNA sequencing showed that both STAMPs and EPV@STAMP facilitated the recovery of disturbed gut microflora. Collectively, our work may accelerate the development of the discovery and delivery of precise antimicrobials.

De novo peptides as potential antimicrobial agents

The phenomenon of antimicrobial resistance threatens our ability to treat common infections. The clinical pipeline for new antimicrobials is pretty much dry and hence, there is a need for the development of new antimicrobial agents with low toxicities to help fight resistant microorganisms. This work aimed to design antimicrobial peptides with low toxicities using a database filtering technology and evaluate their bioactivities. The physicochemical properties of the designed peptides were explored with molecular dynamics (MD) simulations. Microbroth dilution and hemolytic assays were used to assess the peptides' antimicrobial activity and toxicity. The activity of combinations of the peptides and some standard antibiotics was tested by the checkerboard method. In general, the designed peptides had a charge of +2, chain length of 13, and hydrophobicity of 61%. The predicted secondary structures of the peptides were either extended conformations or alpha-helices, and these structures were found to fluctuate during the MD simulations, where coils, bends, and helices dominated. , of the peptides, BRG003, BRG004 and BRG002 had the greatest aggregation propensities, whereas BRG001, BRG005, and BRG006 exhibited lower aggregation propensities. The minimum inhibitory concentration (MIC) of the peptides ranged from 0.015 to >1.879 μM, with BRGP-001 exhibiting high activity against MRSA with an MIC of 15 nM. BRGP-005 and BRGP-006 exhibited synergistic effects against Escherichia coliR when used in combination with erythromycin. At the minimum hemolytic concentration, the percentage of lysed erythrocytes was lower for all the peptides in comparison to the reference peptide, indicating low hemolytic activity. The study revealed the importance of peptide self-association in the antimicrobial activity of the peptides. These peptides provide a basis for the design of potent antimicrobial peptides that can further be developed for use in antimicrobial therapy.

Computationally Designed AMPs with Antibacterial and Antibiofilm Activity against MDR Acinetobacter baumannii

The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we utilized a computational AMP design strategy combining database filtering technology plus positional analysis to design a series of novel peptides, named HRZN, designed to be active against A. baumannii. All of the HRZN peptides we synthesized exhibited antimicrobial activity against three MDR A. baumannii strains with HRZN-15 being the most active (MIC 4 µg/mL). This peptide also inhibited and eradicated biofilm of A. baumannii strain AB5075 at 8 and 16 µg/mL, which is highly effective. HRZN-15 permeabilized and depolarized the membrane of AB5075 rapidly, as demonstrated by the killing kinetics. HRZN 13 and 14 peptides had little to no hemolysis activity against human red blood cells, whereas HRZN-15, -16, and -17 peptides demonstrated more significant hemolytic activity. HRZN-15 also demonstrated toxicity to waxworms. Further modification of HRZN-15 could result in a new peptide with an improved toxicity profile. Overall, we successfully designed a set of new AMPs that demonstrated activity against MDR A. baumannii using a computational approach.

Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides

Introduction

Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends.

Methods

Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs.

Results

EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis.

Conclusion

The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs.

Antimicrobial Peptides and Small Molecules Targeting the Cell Membrane of Staphylococcus aureus

Clinical management of Staphylococcus aureus infections presents a challenge due to the high incidence, considerable virulence, and emergence of drug resistance mechanisms. The treatment of drug-resistant strains, such as methicillin-resistant S. aureus (MRSA), is further complicated by the development of tolerance and persistence to antimicrobial agents in clinical use. To address these challenges, membrane disruptors, that are not generally considered during drug discovery for agents against S. aureus, should be explored. The cell membrane protects S. aureus from external stresses and antimicrobial agents, but membrane-targeting antimicrobial agents are probably less likely to promote bacterial resistance. Nontypical linear cationic antimicrobial peptides (AMPs), highly modified AMPs such as daptomycin (lipopeptide), bacitracin (cyclic peptide), and gramicidin S (cyclic peptide), are currently in clinical use. Recent studies have demonstrated that AMPs and small molecules can penetrate the cell membrane of S. aureus, inhibit phospholipid biosynthesis, or block the passage of solutes between the periplasm and the exterior of the cell. In addition to their primary mechanism of action (MOA) that targets the bacterial membrane, AMPs and small molecules may also impact bacteria through secondary mechanisms such as targeting the biofilm, and downregulating virulence genes of S. aureus. In this review, we discuss the current state of research into cell membrane-targeting AMPs and small molecules and their potential mechanisms of action against drug-resistant physiological forms of S. aureus, including persister cells and biofilms.

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.

Bioinformatics Approaches Applied to the Discovery of Antifungal Peptides

Antifungal peptides (AFPs) comprise a group of substances with a broad spectrum of activities and complex action mechanisms. They develop in nature via an evolutionary process resulting from the interactions between hosts and pathogens. The AFP database is experimentally verified and curated from research articles, patents, and public databases. In this review, we compile information about the primary databases and bioinformatics tools that have been used in the discovery of AFPs during the last 15 years. We focus on the classification and prediction of AFPs using different physicochemical properties, such as polarity, hydrophobicity, hydrophilicity, mass, acidic, basic, and isoelectric indices, and other structural properties. Another method for discovering AFPs is the implementation of a peptidomic approach and bioinformatics filtering, which gave rise to a new family of peptides that exhibit a broad spectrum of antimicrobial activity against Candida albicans with low hemolytic effects. The application of machine intelligence in the sphere of biological sciences has led to the development of automated tools. The progress made in this area has also paved the way for producing new drugs more quickly and effectively. However, we also identified that further advancements are still needed to complete the AFP libraries.

Temporins: Multifunctional Peptides from Frog Skin

Temporins are short peptides secreted by frogs from all over the world. They exert antimicrobial activity, mainly against Gram-positive bacteria, including resistant pathogens; recent studies highlight other possible applications of these peptides as anticancer or antiviral agents. This review is meant to describe the main features of temporins produced by different ranid genera. Due to the abundance of published papers, we focus on the most widely investigated peptides. We report studies on their mechanism of action and three-dimensional structure in model systems mimicking bacterial membranes or in the presence of cells. The design and the antimicrobial activity of peptide analogues is also described, with the aim of highlighting elements that are crucial to improve the bioactivity of peptides while reducing their toxicity. Finally, a short section is dedicated to the studies aimed at applying these peptides as drugs, to produce new antimicrobial materials or in other technological uses.

Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

Evaluation of the Synthetic Multifunctional Peptide Hp-MAP3 Derivative of Temporin-PTa

In recent years, antimicrobial peptides isolated from amphibian toxins have gained attention as new multifunctional drugs interacting with different molecular targets. We aimed to rationally design a new peptide from temporin-PTa. Hp-MAP3 (NH2-LLKKVLALLKKVL-COOH), net charge (+4), hydrophobicity (0.69), the content of hydrophobic residues (69%), and hydrophobic moment (0.73). For the construction of the analog peptide, the physicochemical characteristics were reorganized into hydrophilic and hydrophobic residues with the addition of lysines and leucines. The minimum inhibitory concentration was 2.7 to 43 μM against the growth of Gram-negative and positive bacteria, and the potential for biofilm eradication was 173.2 μM. Within 20 min, the peptide Hp-MAP3 (10.8 μM) prompted 100% of the damage to E. coli cells. At 43.3 μM, eliminated 100% of S. aureus within 5 min. The effects against yeast species of the Candida genus ranged from 5.4 to 86.6 μM. Hp-MAP3 presents cytotoxic activity against tumor HeLa at a concentration of 21.6 μM with an IC50 of 10.4 µM. Furthermore, the peptide showed hemolytic activity against murine erythrocytes. Structural studies carried out by circular dichroism showed that Hp-MAP3, while in the presence of 50% trifluoroethanol or SDS, an α-helix secondary structure. Finally, Amphipathic Hp-MAP3 building an important model for the design of new multifunctional molecules.

Deciphering Structure-Function Relationship Unveils Salt-Resistant Mode of Action of a Potent MRSA-Inhibiting Antimicrobial Peptide, RR14

Multidrug-resistant (MDR) bacteria lead to considerable morbidity and mortality, threatening public health worldwide. In particular, infections of methicillin-resistant Staphylococcus aureus (MRSA) in hospital and community settings are becoming a serious health problem. Antimicrobial peptides (AMPs) are considered novel therapeutic targets against MDR bacteria. However, salt sensitivity reduces the bactericidal potency of AMPs, posing a major obstacle for their development as antibiotics. Thus, the design and development of salt-insensitive peptides with potent antibacterial activity is imperative. Here, we employed biochemical and biophysical examinations coupled with molecular modeling to systematically investigate the structure-function relationship of a novel salt-insensitive AMP, RR14. The secondary structure of RR14 was characterized as an apparent α-helix, a structure that confers strong membrane-permeabilizing ability targeting bacterial-mimetic membranes. Additionally, the bioactive structure of RR14 was determined in complex with dodecylphosphocholine (DPC) micelles, where it possesses a central α-helical segment comprising residues R4 to K13 (R4-K13). RR14 was observed to orient itself into the DPC micelle with its N terminus and the α-helical segment (I5-R10) buried inside the micelles, which is essential for membrane permeabilization and bactericidal activity. Moreover, the specific and featured arrangement of positively charged residues of RR14 on its amphipathic helical conformation has great potential to render its strong salt resistance ability. Our study explored the structure-function relationship of RR14, explaining its possible mode of action against MRSA and other microbes. The insights obtained are of great applicability for the development of new antibacterial agents. IMPORTANCE Many antimicrobial peptides have been observed to become inactive in the presence of high salt concentrations. To further develop new and novel AMPs with potent bactericidal activity and salt insensitivity, understanding the structural basis for salt resistance is important. Here, we employed biochemical and biophysical examinations to systematically investigate the structure-function relationship of a novel salt-insensitive AMP, RR14. RR14 was observed to orient itself into DPC micelles with the N terminus and the α-helical segment (I5-R10) buried inside the micelles, which is essential for membrane permeabilization and bactericidal activity. Moreover, the specific and featured arrangement of cationic residues of RR14 on its amphipathic helical conformation renders its strong salt resistance ability. The insights obtained are of great applicability for developing new antibacterial agents.

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.

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.

Evaluation of a Novel Synthetic Peptide Derived from Cytolytic Mycotoxin Candidalysin

The importance of neuroinflammation in neurology is becoming increasingly apparent. In addition to neuroinflammatory diseases such as multiple sclerosis, the role of neuroinflammation has been identified in many non-inflammatory neurological disorders such as stroke, epilepsy, and cancer. The immune response within the brain involves the presence of CNS resident cells; mainly glial cells, such as microglia, the CNS resident macrophages. We evaluated the peptide Ca-MAP1 bioinspired on the C. albicans immature cytolytic toxin candidalysin to develop a less hemolytic peptide with anti-neuroinflammatory, antibacterial, and cytotoxic activity against tumor cells. In silico and in vitro studies were performed at various concentrations. Ca-MAP1 exhibits low hemolytic activity at lower concentrations and was not cytotoxic to MRC-5 and BV-2 cells. Ca-MAP1 showed activity against Acinetobacter baumannii, Escherichia coli ATCC, E. coli KPC, Klebsiella pneumoniae ATCC, Pseudomonas aeruginosa, and Staphylococcus aureus ATCC. Furthermore, Ca-MAP1 exhibits anti-neuroinflammatory activity in the BV-2 microglia model, with 93.78% inhibition of nitrate production at 18.1 µM. Ca-MAP1 presents cytotoxic activity against tumor cell line NCI-H292 at 36.3 μM, with an IC₅₀ of 38.4 µM. Ca-MAP1 demonstrates results that qualify it to be evaluated in the next steps to promote the control of infections and provide an alternative antitumor therapy.

Construction of Smart Higher Education Teaching Resources Using Data Analysis Technology in Unbalanced Data Environment

The difficulty in gathering teaching resources presents challenges in the process of developing instructional materials for smart higher education. This essay makes a research proposal for a study using data mining technology to create instructional materials for smart higher education. The analysis of the dynamic scheduling mechanism of intelligent higher education teaching resources based on data analysis technology in unbalanced data environment follows research on the establishment of teaching materials from the discovery of teaching materials, the marking of teaching materials, and the organization of teaching materials. In the end, it is determined that class A students' grades are unquestionably higher than those of class B students. Of course, there are some class B students who score higher than average, but class B students tend to score between 50 and 60 points on average, whereas class A students tend to score higher than average. The contrast is greater, and there are more pupils scoring between 90 and 100. The average grade for students in class A is 80.125, whereas the average grade for students in class B is 71.45. The lowest score in Class B is 51, the lowest score in A is 58, and the greatest score in A is up to 98. It is clear that the development of intelligent teaching resources for higher education based on data mining technology is very successful and has been thoroughly proven.

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.

Linearized teixobactin is inactive and after sequence enhancement, kills methicillin-resistant Staphylococcus aureus via a different mechanism

Staphylococcus aureus is a highly adaptable pathogen that can rapidly develop resistance to conventional antibiotics such as penicillin. Recently, teixobactin was discovered from uncultivated soil bacteria by using the i-chip technology. This depsipeptide forms an ester bond between the backbone C-terminal isoleucine carboxylic acid and the hydroxyl group of threonine at position 8. Also, it contains multiple nonstandard amino acids, making it costly to synthesize. This study reports new peptides designed by linearizing teixobactin. After linearization and conversion to normal amino acids, teixobactin lost its antibacterial activity. Using this inactive template, a series of peptides were designed via hydrophobic patching and residue replacements. Three out of the five peptides were active. YZ105, only active against Gram-positive bacteria, however, showed the highest cell selectivity index. Different from teixobactin, which inhibits cell wall synthesis, YZ105 targeted the membranes of methicillin-resistant S. aureus (MRSA) based on kinetic killing, membrane permeation, depolarization, and scanning electron microscopy studies. Moreover, YZ105 could kill nafcillin-resistant MRSA, Staphylococcal clinical strains, and disrupted preformed biofilms. Taken together, YZ105, with a simpler sequence, is a promising lead for developing novel anti-MRSA agents.

Design and Evaluation of Short Bovine Lactoferrin-Derived Antimicrobial Peptides against Multidrug-Resistant Enterococcus faecium

Enterococcus faecium has become an important drug-resistant nosocomial pathogen because of widespread antibiotic abuse. We developed short and chemically simple antimicrobial peptides (AMPs) with a selective amino acid composition, fixed charge, and hydrophobicity ratio based on the core antimicrobial motif of bovine lactoferrin (LfcinB6). Among these peptides, 5L and 6L (both 12 residues long) demonstrated a narrow spectrum and high antibacterial activity against drug-resistant E. faecium isolates with a minimal inhibitory concentration (MIC) that ranged from 4–16 µg/mL. At 32 µg/mL, peptides 5L and 6L inhibited E. faecium strain C68 biofilm formation by 90% and disrupted established biofilms by 75%. At 40 µg/mL, 5L reduced 1 × 107E. faecium persister cells by 3 logs within 120 min of exposure, whereas 6L eliminated all persister cells within 60 min. At 0.5× MIC, 5L and 6L significantly downregulated the expression of a crucial biofilm gene ace by 8 folds (p = 0.02) and 4 folds (p = 0.01), respectively. At 32 µg/mL, peptides 5L and 6L both depolarized the E. faecium membrane, increased fluidity, and eventually ruptured the membrane. Physiologically, 5L (at 8 µg/mL) altered the tricarboxylic acid cycle, glutathione, and purine metabolism. Interestingly, in an ex vivo model of porcine skin infection, compared to no treatment, 5L (at 10× MIC) effectively eliminated all 1 × 106 exponential (p = 0.0045) and persister E. faecium cells (p = 0.0002). In conclusion, the study outlines a roadmap for developing narrow-spectrum selective AMPs and presents peptide 5L as a potential therapeutic candidate to be explored against E. faecium.

Targeted Antimicrobial Agents as Potential Tools for Modulating the Gut Microbiome

The gut microbiome plays a pivotal role in maintaining the health of the hosts; however, there is accumulating evidence that certain bacteria in the host, termed pathobionts, play roles in the progression of diseases. Although antibiotics can be used to eradicate unwanted bacteria, the side effects of antibiotic treatment lead to a great need for more targeted antimicrobial agents as tools to modulate the microbiome more precisely. Herein, we reviewed narrow-spectrum antibiotics naturally made by plants and microorganisms, followed by more targeted antibiotic agents including synthetic peptides, phage, and targeted drug delivery systems, from the perspective of using them as potential tools for modulating the gut microbiome for favorable effects on the health of the host. Given the emerging discoveries on pathobionts and the increasing knowledge on targeted antimicrobial agents reviewed in this article, we anticipate targeted antimicrobial agents will emerge as a new generation of a drug to treat microbiome-involved diseases.

Synthetic peptides bioinspired in temporin-PTa with antibacterial and antibiofilm activity

Several antimicrobial peptides (AMPs) have been reported in amphibian toxins, as temporin-PTa from Hylarana picturata. The amino acid distribution within a helical structure of AMPs favors the design of new bioactive peptides. Therefore, this work reports the rational design of two new synthetic peptides denominated Hp-MAP1 and Hp-MAP2 derived from temporin-PTa. These peptides present an amphipathic helix with positive charges of +4 and +5, hydrophobic moment (<µH>) of 0.66 and 0.72 and hydrophobicity (<H>) of 0.49 and 0.41, respectively. Hp-MAP1 and Hp-MAP2 displayed in vitro activity against Gram-negative and Gram-positive bacteria from 2.8 to 92 µM, without presenting hemolytic effects. Molecular dynamics simulation suggested that the parent and designed temporin-like peptides lack structural stability in an aqueous solution. By contrast, α-helical structures were predicted in hydrophobic and anionic environments. Additionally, the peptides were simulated on mimetic membranes composed of anionic and neutral phospholipids 1,2-dipalmitoylsn-glycerol-3-phosphatidylglycerol (DPPG-anionic), 1,2-dipalmitoyl-sn-lyco-3 phosphatidylethanolamine (DPPE-neutral). When in contact with DPPG/DPPE (90:10) and DPPG/DPPE (50:50) temporin-PTa, Hp-MAP1 and Hp-MAP2 established interactions guided by hydrogen and saline bounds. Therefore, the findings described here reveal that the optimization of the amphipathic α-helical cationic peptides Hp-MAP1 and Hp-MAP2 enabled the generation of new synthetic antimicrobial agents to combat pathogenic microorganisms.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

A hyaluronic acid-based nanogel for the co-delivery of nitric oxide (NO) and a novel antimicrobial peptide (AMP) against bacterial biofilms

Biofilms are a global health concern because they are associated with chronic and recurrent infections as well as resistance to conventional antibiotics. The aim of this study was to prepare a nanogel for the co-delivery of NO and AMPs against bacteria and biofilms. The NO-releasing nanogel was prepared by crosslinking HA solution with divinyl sulfone and extensively characterized. The nanogel was found to be biocompatible, injectable and NO release from the gel was sustained over a period of 24 h. In vitro antibacterial studies showed that the NO-AMP-loaded nanogel exhibited a broad spectrum antibacterial/antibiofilm activity. The NO-releasing nanogel had a greater antibacterial effect when compared to NO alone with MIC values of 1.56, 0.78 and 0.39 μg/ml against Escherichia coli, Methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa bacteria respectively. The antibiofilm results showed there was a 12.5 and 24-folds reduction in biofilms of MRSA, and P. aeruginosa respectively for catheters exposed to nanogel loaded with AMP/NO when compared to only NO, while a 7 and 9.4-folds reduction in biofilms of MRSA, and P. aeruginosa respectively was displayed by the nanogel loaded with only NO compared to only NO. The AMP/NO-releasing nanogel showed the potential to combat both biofilms and bacterial infections.

A sustainable and efficient recycling strategy of feather waste into keratin peptides with antimicrobial activity

The study aimed to propose an efficient and eco-friendly strategy to improve the utilization of feather waste and converting it into high-valued antimicrobial products. Under the synergistic effect of instant catapult steam explosion (ICSE) (1.5 MPa-120 s), over 90% of chicken feather powder (CFP) was degraded into soluble peptides via keratinolysis within 3 h, about 90% of which were smaller than 3 kDa, indicating an overwhelming advantage than general proteolysis. Importantly, the keratinolysis hydrolysate of CFP was able to inhibit E. coli growth, among which the fraction < 3 kDa exhibited highest antimicrobial activity with a minimal inhibitory concentration of 30 mg/mL. Compared to other fractions, the fraction < 3 kDa contained higher content of hydrophobic amino acids (364.11 mg/g), in which about 79% of peptides had more than 60% hydrophobic ratio, potentially contributing to its antimicrobial activity. ICSE-keratinolysis process holds potential in reducing both protein resource waste and environmental pollution by valorizing feathers into antimicrobial product.

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants.

Synthetic molecular evolution of antimicrobial peptides

As we learn more about how peptide structure and activity are related, we anticipate that antimicrobial peptides will be engineered to have strong potency and distinct functions and that synthetic peptides will have new biomedical applications, such as treatments for emerging infectious diseases. As a result of the enormous number of possible amino acid sequences and the low-throughput nature of antimicrobial peptide assays, computational tools for peptide design and optimization are needed for direct experimentation toward obtaining functional sequences. Recent developments in computational tools have improved peptide design, saving labor, reagents, costs, and time. At the same time, improvements in peptide synthesis and experimental platforms continue to reduce the cost and increase the throughput of peptide-drug screening. In this review, we discuss the current methods of peptide design and engineering, including in silico methods and peptide synthesis and screening, and highlight areas of potential improvement.

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

A review on antimicrobial peptides databases and the computational tools

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application

A multitude of membrane active peptides exists that divides into subclasses, such as cell penetrating peptides (CPPs) capable to enter eukaryotic cells or antimicrobial peptides (AMPs) able to interact with prokaryotic cell envelops. Peptide membrane interactions arise from unique sequence motifs of the peptides that account for particular physicochemical properties. Membrane active peptides are mainly cationic, often primary or secondary amphipathic, and they interact with membranes depending on the composition of the bilayer lipids. Sequences of these peptides consist of short 5–30 amino acid sections derived from natural proteins or synthetic sources. Membrane active peptides can be designed using computational methods or can be identified in screenings of combinatorial libraries. This review focuses on strategies that were successfully applied to the design and optimization of membrane active peptides with respect to the fact that diverse features of successful peptide candidates are prerequisites for biomedical application. Not only membrane activity but also degradation stability in biological environments, propensity to induce resistances, and advantageous toxicological properties are crucial parameters that have to be considered in attempts to design useful membrane active peptides. Reliable assay systems to access the different biological characteristics of numerous membrane active peptides are essential tools for multi-objective peptide optimization.

The evolution of the antimicrobial peptide database over 18 years: Milestones and new features

The antimicrobial peptide database (APD) has served the antimicrobial peptide field for 18 years. Because it is widely used in research and education, this article documents database milestones and key events that have transformed it into the current form. A comparison is made for the APD peptide statistics between 2010 and 2020, validating the major database findings to date. We also describe new additions ranging from peptide entries to search functions. Of note, the APD also contains antimicrobial peptides from host microbiota, which are important in shaping immune systems and could be linked to a variety of human diseases. Finally, the database has been re-programmed to the web branding and latest security compliance of the University of Nebraska Medical Center. The reprogrammed APD can be accessed at https://aps.unmc.edu.

Machine Learning Prediction of Antimicrobial Peptides

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

Lysine Stapling Screening Provides Stable and Low Toxic Cationic Antimicrobial Peptides Combating Multidrug-Resistant Bacteria In Vitro and In Vivo

Cationic antimicrobial peptides (CAMPs) are promising for treatment of multidrug-resistant (MDR) bacteria-caused infections. However, clinical application of CAMPs has been hampered mostly due to their poor proteolytic stability and hemolytic toxicity. Recently, lysine-stapled CAMPs developed by us had been proved to increase peptide stability in vitro without induction of hemolysis. Herein, the applicability of the lysine stapling strategy was further explored by using five natural or artificial CAMPs as model peptides. Lysine stapling screening was implemented to provide 13 cyclic analogues in total. Biological screening of these cyclic analogues showed that CAMPs with a better amphiphilic structure were inclined to exhibit improved antimicrobial activity, protease stability, and biocompatibility after lysine-stapling. One of the stapled analogues of BF15-a1 was found to have extended half-life in plasma, enhanced antimicrobial activity against clinically isolated MDR ESKAPE pathogens, and remained highly effective in combating MRSA infection in a mouse model.

Ab initio Designed Antimicrobial Peptides Against Gram-Negative Bacteria

Antimicrobial peptides (AMPs) are ubiquitous amongst living organisms and are part of the innate immune system with the ability to kill pathogens directly or indirectly by modulating the immune system. AMPs have potential as a novel therapeutic against bacteria due to their quick-acting mechanism of action that prevents bacteria from developing resistance. Additionally, there is a dire need for therapeutics with activity specifically against Gram-negative bacterial infections that are intrinsically difficult to treat, with or without acquired drug resistance. Development of new antibiotics has slowed in recent years and novel therapeutics (like AMPs) with a focus against Gram-negative bacteria are needed. We designed eight novel AMPs, termed PHNX peptides, using ab initio computational design (database filtering technology combined with the novel positional analysis on APD3 dataset of AMPs with activity against Gram-negative bacteria) and assessed their theoretical function using published machine learning algorithms, and finally, validated their activity in our laboratory. These AMPs were tested to establish their minimum inhibitory concentration (MIC) and half-maximal effective concentration (EC₅₀) under CLSI methodology against antibiotic resistant and antibiotic susceptible Escherichia coli and Staphylococcus aureus. Laboratory-based experimental results were compared to computationally predicted activities for each of the peptides to ascertain the accuracy of the computational tools used. PHNX-1 demonstrated antibacterial activity (under high and low-salt conditions) against antibiotic resistant and susceptible strains of Gram-positive and Gram-negative bacteria and PHNX-4 to -8 demonstrated low-salt antibacterial activity only. The AMPs were then evaluated for cytotoxicity using hemolysis against human red blood cells and demonstrated some hemolysis which needs to be further evaluated. In this study, we successfully developed a design methodology to create synthetic AMPs with a narrow spectrum of activity where the PHNX AMPs demonstrated higher antibacterial activity against Gram-negative bacteria compared to Gram-positive bacteria. Thus, these peptides present novel synthetic peptides with a potential for therapeutic use. Based on our findings, we propose upfront selection of the peptide dataset for analysis, an additional step of positional analysis to add to the ab initio database filtering technology (DFT) method, and we present laboratory data on the novel, synthetically designed AMPs to validate the results of the computational approach. We aim to conduct future in vivo studies which could establish these AMPs for clinical use.

Natural antimicrobial peptides as a source of new antiviral agents

Current antiviral drugs are limited because of their adverse side effects and increased rate of resistance. In recent decades, much scientific effort has been invested in the discovery of new synthetic and natural compounds with promising antiviral properties. Among this new generation of compounds, antimicrobial peptides with antiviral activity have been described and are attracting attention due to their mechanism of action and biological properties. To understand the potential of antiviral peptides (AVPs), we analyse the antiviral activity of well-known AVP families isolated from different natural sources, discuss their physical-chemical properties, and demonstrate how AVP databases can guide us to design synthetic AVPs with better therapeutic properties. All considerations in this sphere of antiviral therapy clearly demonstrate the remarkable contribution that AVPs may make in conquering old as well as newly emerging viruses that plague humanity.

Chitosan-Based Hydrogel for the Dual Delivery of Antimicrobial Agents Against Bacterial Methicillin-Resistant Staphylococcus aureus Biofilm-Infected Wounds

Chronic wound infections caused by antibiotic-resistant bacteria have become a global health concern. This is attributed to the biofilm-forming ability of bacteria on wound surfaces, thus enabling their persistent growth. In most cases, it leads to morbidity and in severe cases mortality. Current conventional approaches used in the treatment of biofilm wounds are proving to be ineffective due to limitations such as the inability to penetrate the biofilm matrix; hence, biofilm-related wounds remain a challenge. Therefore, there is a need for more efficient alternate therapeutic interventions. Hydrogen peroxide (HP) is a known antibacterial/antibiofilm agent; however, prolonged delivery has been challenging due to its short half-life. In this study, we developed a hydrogel for the codelivery of HP and antimicrobial peptides (Ps) against bacteria, biofilms, and wound infection associated with biofilms. The hydrogel was prepared via the Michael addition technique, and the physiochemical properties were characterized. The safety, in vitro, and in vivo antibacterial/antibiofilm activity of the hydrogel was also investigated. Results showed that the hydrogel is biosafe. A greater antibacterial effect was observed with HP-loaded hydrogels (CS-HP; hydrogel loaded with HP and CS-HP-P; hydrogel loaded with HP and peptide) when compared to HP as seen in an approximately twofold and threefold decrease in minimum inhibitory concentration values against methicillin-resistant Staphylococcus aureus (MRSA) bacteria, respectively. Similarly, both the HP-releasing hydrogels showed enhanced antibiofilm activity in the in vivo study in mice models as seen in greater wound closure and enhanced wound healing in histomorphological analysis. Interestingly, the results revealed a synergistic antibacterial/antibiofilm effect between HP and P in both in vitro and in vivo studies. The successfully prepared HP-releasing hydrogels showed the potential to combat bacterial biofilm-related infections and enhance wound healing in mice models. These results suggest that the HP-releasing hydrogels may be a superior platform for eliminating bacterial biofilms without using antibiotics in the treatment of chronic MRSA wound infections, thus improving the quality of human health.

Potential antibacterial ethanol-bridged purine azole hybrids as dual-targeting inhibitors of MRSA

A new class of antibacterial ethanol-bridged purine azole hybrids as potential dual-targeting inhibitors was developed. Bioactivity evaluation showed that some of the target compounds had prominent antibacterial activity against the tested bacteria, notably, metronidazole hybrid 3a displayed significant inhibitory activity against MRSA (MIC = 6 μM), and had no obvious toxicity on normal mammalian cells (RAW 264.7). In addition, compound 3a also did not induce drug resistance of MRSA obviously, even after fifteen passages. Molecular modeling studies showed that the highly active molecule 3a could insert into the base pairs of topoisomerase IA-DNA as well as topoisomerase IV-DNA through hydrogen bonding. Furthermore, a preliminary study on the antibacterial mechanism revealed that the active molecule 3a could rupture the bacterial membrane of MRSA and insert into MRSA DNA to block its replication, thus possibly exhibiting strong antibacterial activity. These results strongly indicated that the highly active hybrid 3a could be used as a potential dual-targeting inhibitor of MRSA for further development of valuable antimicrobials.

Novel chalcone-conjugated, multi-flexible end-group coumarin thiazole hybrids as potential antibacterial repressors against methicillin-resistant Staphylococcus aureus

The increasing resistance of methicillin-resistant Staphylococcus aureus (MRSA) to antibiotics has led to a growing effort to design and synthesize novel structural candidates of chalcone-conjugated, multi-flexible end-group coumarin thiazole hybrids with outstanding bacteriostatic potential. Bioactivity screening showed that hybrid 5i, which was modified with methoxybenzene, exerted a significant inhibitory activity against MRSA (MIC = 0.004 mM), which was 6 times better than the anti-MRSA activity of the reference drug norfloxacin (MIC = 0.025 mM). Compound 5i neither conferred apparent resistance onto MRSA strains even after multiple passages nor triggered evident toxicity to human hepatocyte LO2 cells and normal mammalian cells (RAW 264.7). Molecular docking showed that highly active molecule 5i might bind to DNA gyrase by forming stable hydrogen bonds. In addition, molecular electrostatic potential surfaces were developed to explain the high antibacterial activity of the target compounds. Furthermore, preliminary mechanism studies suggested that hybrid 5i could disrupt the bacterial membrane of MRSA and insert itself into MRSA DNA to impede its replication, thus possibly becoming a potential antibacterial repressor against MRSA.

Emerging Emulsifiers: Conceptual Basis for the Identification and Rational Design of Peptides with Surface Activity

Emulsifiers are gradually evolving from synthetic molecules of petrochemical origin to biomolecules mainly due to health and environmental concerns. Peptides represent a type of biomolecules whose molecular structure is composed of a sequence of amino acids that can be easily tailored to have specific properties. However, the lack of knowledge about emulsifier behavior, structure-performance relationships, and the implementation of different design routes have limited the application of these peptides. Some computational and experimental approaches have tried to close this knowledge gap, but restrictions in understanding the fundamental phenomena and the limited property data availability have made the performance prediction for emulsifier peptides an area of intensive research. This study provides the concepts necessary to understand the emulsifying behavior of peptides. Additionally, a straightforward description is given of how the molecular structure and conditions of the system directly impact the peptides' ability to stabilize emulsion droplets. Moreover, the routes to design and discover novel peptides with interfacial and emulsifying activity are also discussed, along with the strategies to address some of their major pitfalls and challenges. Finally, this contribution reviews methodologies to build and use data sets containing standard properties of emulsifying peptides by looking at successful applications in different fields.

Amphipathic Peptide Antibiotics with Potent Activity against Multidrug-Resistant Pathogens

The emergence and prevalence of multidrug-resistant (MDR) bacteria have posed a serious threat to public health. Of particular concern are methicillin-resistant Staphylococcus aureus (MRSA) and blaNDM, mcr-1 and tet(X)-positive Gram-negative pathogens. The fact that few new antibiotics have been approved in recent years exacerbates this global crisis, thus, new alternatives are urgently needed. Antimicrobial peptides (AMPs) originated from host defense peptides with a wide range of sources and multiple functions, are less prone to achieve resistance. All these characteristics laid the foundation for AMPs to become potential antibiotic candidates. In this study, we revealed that peptide WW307 displayed potent antibacterial and bactericidal activity against MDR bacteria, including MRSA and Gram-negative bacteria carrying blaNDM-5, mcr-1 or tet(X4). In addition, WW307 exhibited great biofilm inhibition and eradication activity. Safety and stability experiments showed that WW307 had a strong resistance against various physiological conditions and displayed relatively low toxicity. Mechanistic experiments showed that WW307 resulted in membrane damage by selectively targeting bacterial membrane-specific components, including lipopolysaccharide (LPS), phosphatidylglycerol (PG), and cardiolipin (CL). Moreover, WW307 dissipated membrane potential and triggered the production of reactive oxygen species (ROS). Collectively, these results demonstrated that WW307 represents a promising candidate for combating MDR pathogens.

Tuning of a Membrane-Perforating Antimicrobial Peptide to Selectively Target Membranes of Different Lipid Composition

The use of designed antimicrobial peptides as drugs has been impeded by the absence of simple sequence-structure-function relationships and design rules. The likely cause is that many of these peptides permeabilize membranes via highly disordered, heterogeneous mechanisms, forming aggregates without well-defined tertiary or secondary structure. We suggest that the combination of high-throughput library screening with atomistic computer simulations can successfully address this challenge by tuning a previously developed general pore-forming peptide into a selective pore-former for different lipid types. A library of 2916 peptides was designed based on the LDKA template. The library peptides were synthesized and screened using a high-throughput orthogonal vesicle leakage assay. Dyes of different sizes were entrapped inside vesicles with varying lipid composition to simultaneously screen for both pore size and affinity for negatively charged and neutral lipid membranes. From this screen, nine different LDKA variants that have unique activity were selected, sequenced, synthesized, and characterized. Despite the minor sequence changes, each of these peptides has unique functional properties, forming either small or large pores and being selective for either neutral or anionic lipid bilayers. Long-scale, unbiased atomistic molecular dynamics (MD) simulations directly reveal that rather than rigid, well-defined pores, these peptides can form a large repertoire of functional dynamic and heterogeneous aggregates, strongly affected by single mutations. Predicting the propensity to aggregate and assemble in a given environment from sequence alone holds the key to functional prediction of membrane permeabilization.