Molecular Dynamics for Antimicrobial Peptide Discovery

Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies

Antimicrobial peptides (AMPs) are increasingly recognized as potent therapeutic agents, with their selective affinity for pathological membranes, low toxicity profile, and minimal resistance development making them particularly attractive in the pharmaceutical landscape. This study offers a comprehensive analysis of the interaction between specific AMPs, including magainin-2, pleurocidin, CM15, LL37, and clavanin, with lipid bilayer models of very different compositions that have been ordinarily used as biological membrane models of healthy mammal, cancerous, and bacterial cells. Employing unbiased molecular dynamics simulations and metadynamics techniques, we have deciphered the intricate mechanisms by which these peptides recognize pathogenic and pathologic lipid patterns and integrate into lipid assemblies. Our findings reveal that the transverse component of the peptide's hydrophobic dipole moment is critical for membrane interaction, decisively influencing the molecule's orientation and expected therapeutic efficacy. Our approach also provides insight on the kinetic and dynamic dependence on the peptide orientation in the axial and azimuthal angles when coming close to the membrane. The aim is to establish a robust framework for the rational design of peptide-based, membrane-targeted therapies, as well as effective quantitative descriptors that can facilitate the automated design of novel AMPs for these therapies using machine learning methods.

Unlocking bacterial defense: Exploring the potent inhibition of NorA efflux pump by coumarin derivatives in Staphylococcus aureus

The occurrence of bacterial resistance has been increasing, compromising the treatment of various infections. The high virulence of Staphylococcus aureus allows for the maintenance of the infectious process, causing many deaths and hospitalizations. The MepA and NorA efflux pumps are transporter proteins responsible for expelling antimicrobial agents such as fluoroquinolones from the bacterial cell. Coumarins are phenolic compounds that have been studied for their diverse biological actions, including against bacteria. A pharmacokinetic in silico characterization of compounds C10, C11, C13, and C14 was carried out according to the principles of Lipinski's Rule of Five, in addition to searching for similarity in ChemBL and subsequent search for publications in CAS SciFinder. All compounds were evaluated for their in vitro antibacterial and modulatory activity against standard and multidrug-resistant Gram-positive and Gram-negative strains. The effect of coumarins C9, C10, C11, C13, and C14 as efflux pump inhibitors in Staphylococcus aureus strains was evaluated using the microdilution method (MepA or NorA) and fluorimetry (NorA). The behavior of coumarins regarding the efflux pump was determined from their interaction properties with the membrane and coumarin-protein using molecular docking and molecular dynamics simulations. Only the isolated coumarin compound C13 showed antibacterial activity against standard strains of Staphylococcus aureus and Escherichia coli. However, the other tested coumarins showed modulatory capacity for fluoroquinolone and aminoglycoside antibacterials. Compounds C10, C13, and C14 were effective in reducing the MIC of both antibiotics for both multidrug-resistant strains, while C11 potentiated the effect of norfloxacin and gentamicin for Gram-positive and Gram-negative bacteria and only norfloxacin for Gram-negative. Only coumarin C14 produced synergistic effects when associated with ciprofloxacin in MepA-carrying strains. All tested coumarins have the ability to inhibit the NorA efflux pump present in Staphylococcus aureus, both in reducing the MIC and inducing increased ethidium bromide fluorescence emission in fluorimetry. The findings of this study offer an atomistic perspective on the potential of coumarins as active inhibitors of the NorA pump, highlighting their specific mode of action mainly targeting protein inhibition. In molecular docking, it was observed that coumarins are capable of interacting with various amino acid residues of the NorA pump. The simulation showed that coumarin C10 can cross the bilayer; however, the other coumarins interacted with the membrane but were unable to cross it. Coumarins demonstrated their potentiating role in the effect of norfloxacin through a dual mechanism: efflux pump inhibition through direct interaction with the protein (C9, C10, C11, and C13) and increased interaction with the membrane (C10 and C13). In the context of pharmacokinetic prediction studies, the studied structures have a suitable chemical profile for possible oral use. We suggest that coumarin derivatives may be an interesting alternative in the future for the treatment of resistant bacterial infections, with the possibility of a synergistic effect with other antibacterials, although further studies are needed to characterize their therapeutic effects and toxicity.

Vesicle protrusion induced by antimicrobial peptides suggests common carpet mechanism for short antimicrobial peptides

Short-cationic alpha-helical antimicrobial peptides (SCHAMPs) are promising candidates to combat the growing global threat of antimicrobial resistance. They are short-sequenced, selective against bacteria, and have rapid action by destroying membranes. A full understanding of their mechanism of action will provide key information to design more potent and selective SCHAMPs. Molecular Dynamics (MD) simulations are invaluable tools that provide detailed insights into the peptide-membrane interaction at the atomic- and meso-scale level. We use atomistic and coarse-grained MD to look into the exact steps that four promising SCHAMPs-BP100, Decoralin, Neurokinin-1, and Temporin L-take when they interact with membranes. Following experimental set-ups, we explored the effects of SCHAMPs on anionic membranes and vesicles at multiple peptide concentrations. Our results showed all four peptides shared similar binding steps, initially binding to the membrane through electrostatic interactions and then flipping on their axes, dehydrating, and inserting their hydrophobic moieties into the membrane core. At higher concentrations, fully alpha-helical peptides induced membrane budding and protrusions. Our results suggest the carpet mode of action is fit for the description of SCHAMPs lysis activity and discuss the importance of large hydrophobic residues in SCHAMPs design and activity.

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.

Deciphering the structure and mechanism of action of computer-designed mastoparan peptides

Mastoparans are cationic peptides with multifunctional pharmacological properties. Mastoparan-R1 and mastoparan-R4 were computationally designed based on native mastoparan-L from wasps and have improved therapeutic potential for the control of bacterial infections. Here, we evaluated whether these peptides maintain their activity against Escherichia coli strains under a range of salt concentrations. We found that mastoparan-R1 and mastoparan-R4 preserved their activity under the conditions tested, including having antibacterial activities at physiological salt concentrations. The overall structure of the peptides was investigated using circular dichroism spectroscopy in a range of solvents. No significant changes in secondary structure were observed (random coil in aqueous solutions and α-helix in hydrophobic and anionic environments). The three-dimensional structures of mastoparan-R1 and mastoparan-R4 were elucidated through nuclear magnetic resonance spectroscopy, revealing amphipathic α-helical segments for Leu3-Ile13 (mastoparan-R1) and Leu3-Ile14 (mastoparan-R4). Possible membrane-association mechanisms for mastoparan-R1 and mastoparan-R4 were investigated through surface plasmon resonance and leakage studies with synthetic POPC and POPC/POPG (4:1) lipid bilayers. Mastoparan-L had the highest affinity for both membrane systems, whereas the two analogs had weaker association, but improved selectivity for lysing anionic membranes. This finding was also supported by molecular dynamics simulations, in which mastoparan-R1 and mastoparan-R4 were found to have greater interactions with bacteria-like membranes compared with model mammalian membranes. Despite having a few differences in their functional and structural profiles, the mastoparan-R1 analog stood out with the highest activity, greater bacteriostatic potential, and selectivity for lysing anionic membranes. This study reinforces the potential of mastoparan-R1 as a drug candidate.

Molecular guidelines for promising antimicrobial agents

Antimicrobial resistance presents a pressing challenge to public health, which requires the search for novel antimicrobial agents. Various experimental and theoretical methods are employed to understand drug-target interactions and propose multistep solutions. Nonetheless, efficient screening of drug databases requires rapid and precise numerical analysis to validate antimicrobial efficacy. Diptool addresses this need by predicting free energy barriers and local minima for drug translocation across lipid membranes. In the current study employing Diptool free energy predictions, the thermodynamic commonalities between selected antimicrobial molecules were characterized and investigated. To this end, various clustering methods were used to identify promising groups with antimicrobial activity. Furthermore, the molecular fingerprinting and machine learning approach (ML) revealed common structural elements and physicochemical parameters in these clusters, such as long carbon chains, charged ammonium groups, and low dipole moments. This led to the establishment of guidelines for the selection of effective antimicrobial candidates based on partition coefficients (logP) and molecular mass ranges. These guidelines were implemented within the Reinforcement Learning for Structural Evolution (ReLeaSE) framework, generating new chemicals with desired properties. Interestingly, ReLeaSE produced molecules with structural profiles similar to the antimicrobial agents tested, confirming the importance of the identified features. In conclusion, this study demonstrates the ability of molecular fingerprinting and AI-driven methods to identify promising antimicrobial agents with a broad range of properties. These findings deliver substantial implications for the development of antimicrobial drugs and the ongoing battle against antibiotic-resistant bacteria.

Leveraging artificial intelligence in the fight against infectious diseases

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Geometric deep learning as a potential tool for antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.

No country for old antibiotics! Antimicrobial peptides (AMPs) as next-generation treatment for skin and soft tissue infection

In recent years, the unprecedented rise of bacterial antibiotic resistance together with the lack of adequate therapies have made the treatment of skin infections and chronic wounds challenging, urging the scientific community to focus on the development of new and more efficient treatment strategies. In this context, there is a growing interest in the use of natural molecules with antimicrobial features, capable of supporting wound healing i.e., antimicrobial peptides (AMPs), for the treatment of skin and soft tissue infections. In this review, we give a short overview of the bacterial skin infections as well as some of the classic treatments used for topical application. We then summarize the AMPs classes, stressing the importance of the appropriate selection of the peptides based on their characteristics and physicochemical properties in order to maximize the antibacterial efficacy of the therapeutic systems against multi-drug resistant pathogens. Additionally, the present paper provides a comprehensive and rigorous assessment of the latest clinical trials investigating the efficacy of AMPs in the treatment of skin and soft tissue infections, highlighting the relevant outcomes. Seeking to obtain novel and improved compounds with synergistic activity, while also decreasing some of the known side effects of AMPs, we present two employed strategies using AMPs: (i) AMPs-conjugated nanosystems for systemic and topical drug delivery systems and (ii) antibiotics-peptide conjugates as a strategy to overcome antibiotics resistance. Finally, an important property of some of the AMPs used in wound treatment is highlighted: their ability to help in wound healing by generally promoting cell proliferation and migration, and in some cases re-epithelialization and angiogenesis among others. Thus, as the pursuit of improvement is an ongoing effort, this work presents the advances made in the treatment of skin and soft tissue infections along with their advantages and limitations, while the still remaining challenges are addressed by providing future prospects and strategies to overcome them.

The Application of Hydroxyapatite NPs for Adsorption Antibiotic from Aqueous Solutions: Kinetic, Thermodynamic, and Isotherm Studies

Antibiotic pollution has become a serious concern due to the extensive use of antibiotics, their resistance to removal, and their detrimental effects on aquatic habitats and humans. Hence, developing an efficient antibiotic removal process for aqueous solutions has become vital. Amoxicillin (Amox) is one of the antibiotics that has been efficiently removed from an aqueous solution using hydroxyapatite nanoparticles (HAP NPs). The current study synthesizes and utilizes hydroxyapatite nanoparticles as a cost-effective adsorbent. Adsorbent dose, pH solution, initial Amox concentration, equilibrium time, and temperature are among the factors that have an evident impact on Amox antibiotic adsorption. The (200) mg dose, pH (5), temperature (25) °C, and time (120) min are shown to be the best-optimized values. The nonlinear Langmuir’s isotherm and pseudo-second-order kinetic models with equilibrium capacities of 4.01 mg/g are highly compatible with the experimental adsorption data. The experimental parameters of the thermodynamic analysis show that the Amox antibiotic adsorption onto HAP NPs powder is spontaneous and exothermic.

Bacterial resistance to antibacterial agents: Mechanisms, control strategies, and implications for global health

The spread of bacterial drug resistance has posed a severe threat to public health globally. Here, we cover bacterial resistance to current antibacterial drugs, including traditional herbal medicines, conventional antibiotics, and antimicrobial peptides. We summarize the influence of bacterial drug resistance on global health and its economic burden while highlighting the resistance mechanisms developed by bacteria. Based on the One Health concept, we propose 4A strategies to combat bacterial resistance, including prudent Application of antibacterial agents, Administration, Assays, and Alternatives to antibiotics. Finally, we identify several opportunities and unsolved questions warranting future exploration for combating bacterial resistance, such as predicting genetic bacterial resistance through the use of more effective techniques, surveying both genetic determinants of bacterial resistance and the transmission dynamics of antibiotic resistance genes (ARGs).

Transferring chemical and energetic knowledge between molecular systems with machine learning

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

Application of a deep generative model produces novel and diverse functional peptides against microbial resistance

Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic's world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.

Biologically Active Peptides from Venoms: Applications in Antibiotic Resistance, Cancer, and Beyond

Peptides are potential therapeutic alternatives against global diseases, such as antimicrobial-resistant infections and cancer. Venoms are a rich source of bioactive peptides that have evolved over time to act on specific targets of the prey. Peptides are one of the main components responsible for the biological activity and toxicity of venoms. South American organisms such as scorpions, snakes, and spiders are important producers of a myriad of peptides with different biological activities. In this review, we report the main venom-derived peptide families produced from South American organisms and their corresponding activities and biological targets.

Molecular dynamics simulations to study the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides

The hydrophobic amino acid biphenylalanine (B) plays a key role in the antibacterial activity of ultrashort peptides. In this study, the interactions of tetrapeptide BRBR-NH₂ (BRBR) and pentapeptide BRBRB-NH₂ (BRBRB) with dioleoylphosphatidylcholine/dioleoylphosphatidylglycerol (DOPC/DOPG) mixed model membrane were studied by molecular dynamics simulation to assess the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides. At low peptide concentrations, both peptides presented amphiphilic conformations; residues B of the pentapeptide approached the membrane faster than those of the tetrapeptide and made more contacts with the membrane; BRBRB exhibited stronger membrane affinity than BRBR. However, due to the low peptide concentrations, the effects of these two peptides on the membrane were not significantly different. At high peptide concentrations, the strong affinity of BRBRB made it have more interaction with membrane than BRBR and most residues B of BRBRB inserted into the membrane; BRBRB was more prone to aggregation and caused the membrane more disordered and thinner than BRBR. Hydrophobic residues often act as anchors in the antibacterial activity of ultrashort antimicrobial peptides. Adding a hydrophobic residue B to the C-terminal of BRBR could improve the ability of the peptide to "grasp" the membrane. At high peptide concentrations, the addition of residue B might enhance the antibacterial activity of the peptide. Thus, our results will be helpful in designing efficient antibacterial drugs.

Membrane fluidity, composition, and charge affect the activity and selectivity of the AMP ascaphin-8

Ascaphins are cationic antimicrobial peptides that have been shown to have potential in the treatment of infectious diseases caused by multidrug-resistant pathogens (MDR). However, to date, their principal molecular target and mechanism of action are unknown. Results from peptide prediction software and molecular dynamics simulations confirmed that ascaphin-8 is an alpha-helical peptide. For the first time, the peptide was described as membranotrophic using biophysical approaches including calcein liposome leakage, Laurdan general polarization, and dynamic light scattering. Ascaphin-8's activity and selectivity were modulated by rearranging the spatial distribution of lysine (Var-K5), aspartic acid (Var-D4) residues, or substitution of phenylalanine with tyrosine (Var-Y). The parental peptide and its variants presented high affinity toward the bacterial membrane model (≤2 μM), but lost activity in sterol-enriched membranes (mammal and fungal models, with cholesterol and ergosterol, respectively). The peptide-induced pore size was estimated to be >20 nm in the bacterial model, with no difference among peptides. The same pattern was observed in membrane fluidity (general polarization) assays, where all peptides reduced membrane fluidity of the bacterial model but not in the models containing sterols. The peptides also showed high activity toward MDR bacteria. Moreover, peptide sensitivity of the artificial membrane models compared with pathogenic bacterial isolates were in good agreement.

Design of a specific peptide against phenolic glycolipid-1 from Mycobacterium leprae and its implications in leprosy bacilli entry

BACKGROUND

Mycobacterium leprae, the causative agent of Hansen’s disease, causes neural damage through the specific interaction between the external phenolic glycolipid-1 (PGL-1) and laminin subunit alpha-2 (LAMA2) from Schwann cells.

OBJECTIVE

To design a LAMA2-based peptide that targets PGL-1 from M. leprae.

METHODS

We retrieved the protein sequence of human LAMA2 and designed a specific peptide using the Antimicrobial Peptide Database and physicochemical parameters for antimycobacterial peptide-lipid interactions. We used the AlphaFold2 server to predict its three-dimensional structure, AUTODOCK-VINA for docking, and GROMACS programs for molecular dynamics simulations.

FINDINGS

We analysed 52 candidate peptides from LAMA2, and subsequent screening resulted in a single 60-mer peptide. The mapped peptide comprises four β-sheets and a random coiled region. This peptide exhibits a 45% hydrophobic ratio, in which one-third covers the same surface. Molecular dynamics simulations show that our predicted peptide is stable in aqueous solution and remains stable upon interaction with PGL-1 binding. In addition, we found that PGL-1 has a preference for one of the two faces of the predicted peptide, which could act as the preferential binding site of PGL-1.

MAIN CONCLUSIONS

Our LAMA2-based peptide targeting PGL-1 might have the potential to specifically block this key molecule, suggesting that the preferential region of the peptide is involved in the initial contact during the attachment of leprosy bacilli to Schwann cells.

Identification of Bacterial Membrane Selectivity of Romo1-Derived Antimicrobial Peptide AMPR-22 via Molecular Dynamics

The abuse or misuse of antibiotics has caused the emergence of extensively drug-resistant (XDR) bacteria, rendering most antibiotics ineffective and increasing the mortality rate of patients with bacteremia or sepsis. Antimicrobial peptides (AMPs) are proposed to overcome this problem; however, many AMPs have attenuated antimicrobial activities with hemolytic toxicity in blood. Recently, AMPR-11 and its optimized derivative, AMPR-22, were reported to be potential candidates for the treatment of sepsis with a broad spectrum of antimicrobial activity and low hemolytic toxicity. Here, we performed molecular dynamics (MD) simulations to clarify the mechanism of lower hemolytic toxicity and higher efficacy of AMPR-22 at an atomic level. We found four polar residues in AMPR-11 bound to a model mimicking the bacterial inner/outer membranes preferentially over eukaryotic plasma membrane. AMPR-22 whose polar residues were replaced by lysine showed a 2-fold enhanced binding affinity to the bacterial membrane by interacting with bacterial specific lipids (lipid A or cardiolipin) via hydrogen bonds. The MD simulations were confirmed experimentally in models that partially mimic bacteremia conditions in vitro and ex vivo. The present study demonstrates why AMPR-22 showed low hemolytic toxicity and this approach using an MD simulation would be helpful in the development of AMPs.

Plant Antimicrobial Peptides (PAMPs): Features, Applications, Production, Expression, and Challenges

The quest for an extraordinary array of defense strategies is imperative to reduce the challenges of microbial attacks on plants and animals. Plant antimicrobial peptides (PAMPs) are a subset of antimicrobial peptides (AMPs). PAMPs elicit defense against microbial attacks and prevent drug resistance of pathogens given their wide spectrum activity, excellent structural stability, and diverse mechanism of action. This review aimed to identify the applications, features, production, expression, and challenges of PAMPs using its structure–activity relationship. The discovery techniques used to identify these peptides were also explored to provide insight into their significance in genomics, transcriptomics, proteomics, and their expression against disease-causing pathogens. This review creates awareness for PAMPs as potential therapeutic agents in the medical and pharmaceutical fields, such as the sensitive treatment of bacterial and fungal diseases and others and their utilization in preserving crops using available transgenic methods in the agronomical field. PAMPs are also safe to handle and are easy to recycle with the use of proteases to convert them into more potent antimicrobial agents for sustainable development.

Concerted Rolling and Penetration of Peptides during Membrane Binding

Peptide binding to membranes is common and fundamental in biochemistry and biophysics and critical for applications ranging from drug delivery to the treatment of bacterial infections. However, it is largely unclear, from a theoretical point of view, what peptides of different sequences and structures share in the membrane-binding and insertion process. In this work, we analyze three prototypical membrane-binding peptides (α-helical magainin, PGLa, and β-hairpin tachyplesin) during membrane binding, using molecular details provided by Markov state modeling and microsecond-long molecular dynamics simulations. By leveraging both geometric and data-driven collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions, we reveal how the slowest kinetic process of membrane binding is the dynamic rolling of the peptide from an attached to a fully bound state. These results not only add fundamental knowledge of the theory of how peptides bind to biological membranes but also open new avenues to study general peptides in more complex environments for further applications.

Biosynthesis, Molecular Regulation, and Application of Bacilysin Produced by Bacillus Species

Microbes produce a diverse range of secondary metabolites in response to various environmental factors and interspecies competition. This enables them to become superior in a particular environment. Bacilysin, a dipeptide antibiotic produced by Bacillus species, is active against a broad range of microorganisms. Because of its simple structure and excellent mode of action, i.e., through the inhibition of glucosamine 6-phosphate synthase, it has drawn the attention of researchers. In addition, it acts as a pleiotropic signaling molecule that affects different cellular activities. However, all Bacillus species are not capable of producing bacilysin. The biosynthesis of bacilysin by Bacillus species is not uniform throughout the population; specificity and heterogeneity at both the strain and species levels has been observed. This review discusses how bacilysin is biosynthesized by Bacillus species, the regulators of its biosynthesis, its importance in the host, and the abiotic factors affecting bacilysin production.

Lipid-mediated antimicrobial resistance: a phantom menace or a new hope?

The proposition of a post-antimicrobial era is all the more realistic with the continued rise of antimicrobial resistance. The development of new antimicrobials is failing to counter the ever-increasing rates of bacterial antimicrobial resistance. This necessitates novel antimicrobials and drug targets. The bacterial cell membrane is an essential and highly conserved cellular component in bacteria and acts as the primary barrier for entry of antimicrobials into the cell. Although previously under-exploited as an antimicrobial target, the bacterial cell membrane is attractive for the development of novel antimicrobials due to its importance in pathogen viability. Bacterial cell membranes are diverse assemblies of macromolecules built around a central lipid bilayer core. This lipid bilayer governs the overall membrane biophysical properties and function of its membrane-embedded proteins. This mini-review will outline the mechanisms by which the bacterial membrane causes and controls resistance, with a focus on alterations in the membrane lipid composition, chemical modification of constituent lipids, and the efflux of antimicrobials by membrane-embedded efflux systems. Thorough insight into the interplay between membrane-active antimicrobials and lipid-mediated resistance is needed to enable the rational development of new antimicrobials. In particular, the union of computational approaches and experimental techniques for the development of innovative and efficacious membrane-active antimicrobials is explored.

In Silico and In Vitro Evaluation of the Antimicrobial Potential of Bacillus cereus Isolated from Apis dorsata Gut against Neisseria gonorrhoeae

Antimicrobial resistance is a major public health and development concern on a global scale. The increasing resistance of the pathogenic bacteria Neisseria gonorrhoeae to antibiotics necessitates efforts to identify potential alternative antibiotics from nature, including insects, which are already recognized as a source of natural antibiotics by the scientific community. This study aimed to determine the potential of components of gut-associated bacteria isolated from Apis dorsata, an Asian giant honeybee, as an antibacterial against N. gonorrhoeae by in vitro and in silico methods as an initial process in the stage of new drug discovery. The identified gut-associated bacteria of A. dorsata included Acinetobacter indicus and Bacillus cereus with 100% identity to referenced bacteria from GenBank. Cell-free culture supernatants (CFCS) of B. cereus had a very strong antibacterial activity against N. gonorrhoeae in an in vitro antibacterial testing. Meanwhile, molecular docking revealed that antimicrobial lipopeptides from B. cereus (surfactin, fengycin, and iturin A) had a comparable value of binding-free energy (BFE) with the target protein receptor for N. gonorrhoeae, namely penicillin-binding protein (PBP) 1 and PBP2 when compared with the ceftriaxone, cefixime, and doxycycline. The molecular dynamics simulation (MDS) study revealed that the surfactin remains stable at the active site of PBP2 despite the alteration of the H-bond and hydrophobic interactions. According to this finding, surfactin has the greatest antibacterial potential against PBP2 of N. gonorrhoeae.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Antimicrobial immunotherapeutics: past, present and future

In this age of antimicrobial resistance (AMR) there is an urgent need for novel antimicrobials. One area of recent interest is in developing antimicrobial effector molecules, and even cell-based therapies, based on those of the immune system. In this review, some of the more interesting approaches will be discussed, including immune checkpoint inhibitors, Interferons (IFNs), Granulocyte-Macrophage Colony Stimulating Factor (GM-CSF), Chimeric Antigen Receptor (CAR) T cells, Antibodies, Vaccines and the potential role of trained immunity in protection from and/or treatment of infection.

Microfluidic Tools for Enhanced Characterization of Therapeutic Stem Cells and Prediction of Their Potential Antimicrobial Secretome

Antibiotic resistance is creating enormous attention on the development of new antibiotic-free therapy strategies for bacterial diseases. Mesenchymal stromal stem cells (MSCs) are the most promising candidates in current clinical trials and included in several cell-therapy protocols. Together with the well-known immunomodulatory and regenerative potential of the MSC secretome, these cells have shown direct and indirect anti-bacterial effects. However, the low reproducibility and standardization of MSCs from different sources are the current limitations prior to the purification of cell-free secreted antimicrobial peptides and exosomes. In order to improve MSC characterization, novel label-free functional tests, evaluating the biophysical properties of the cells, will be advantageous for their cell profiling, population sorting, and quality control. We discuss the potential of emerging microfluidic technologies providing new insights into density, shape, and size of live cells, starting from heterogeneous or 3D cultured samples. The prospective application of these technologies to studying MSC populations may contribute to developing new biopharmaceutical strategies with a view to naturally overcoming bacterial defense mechanisms.