Computational Methods and Tools in Antimicrobial Peptide Research

Important structural features of antimicrobial peptides towards specific activity: Trends in the development of efficient therapeutics

Proteins and peptides, as polypeptide chains, have usually got unique conformational structures for effective biological activity. Antimicrobial peptides (AMPs) are a group of bioactive peptides, which have been increasingly studied during recent years for their promising antibacterial, antifungal, antiviral and anti-inflammatory activity, as well as, other esteemed bioactivities. Numerous AMPs have been separated from a wide range of natural resources, or produced in vitro through chemical synthesis and recombinant protein expression. Natural AMPs have had limited clinical application due to several drawbacks, such as their short half-life due to protease degradation, lack of activity at physiological salt concentrations, toxicity to mammalian cells, and the absence of suitable methods of delivery for the AMPs that are targeted and sustained. The creation of synthetic analogs of AMPs would both avoid the drawbacks of the natural analogs and maintain or even increase the antimicrobial effectiveness. The structure-activity relationship of discovered AMPs or their derivatives facilitates the development of synthetic AMPs. This review discovered that the relationship between the activity of AMPs and their positive net charge, hydrophobicity, and amino acid sequence and the relationship between AMPs' function and other features like their topology, glycosylation, and halogenation.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Blap-6, a Novel Antifungal Peptide from the Chinese Medicinal Beetle Blaps rhynchopetera against Cryptococcus neoformans

Cryptococcus neoformans (C. neoformans) is a pathogenic fungus that can cause life-threatening meningitis, particularly in individuals with compromised immune systems. The current standard treatment involves the combination of amphotericin B and azole drugs, but this regimen often leads to inevitable toxicity in patients. Therefore, there is an urgent need to develop new antifungal drugs with improved safety profiles. We screened antimicrobial peptides from the hemolymph transcriptome of Blaps rhynchopetera (B. rhynchopetera), a folk Chinese medicine. We found an antimicrobial peptide named blap-6 that exhibited potent activity against bacteria and fungi. Blap-6 is composed of 17 amino acids (KRCRFRIYRWGFPRRRF), and it has excellent antifungal activity against C. neoformans, with a minimum inhibitory concentration (MIC) of 0.81 μM. Blap-6 exhibits strong antifungal kinetic characteristics. Mechanistic studies revealed that blap-6 exerts its antifungal activity by penetrating and disrupting the integrity of the fungal cell membrane. In addition to its direct antifungal effect, blap-6 showed strong biofilm inhibition and scavenging activity. Notably, the peptide exhibited low hemolytic and cytotoxicity to human cells and may be a potential candidate antimicrobial drug for fungal infection caused by C. neoformans.

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.

Structure of the Bacterial Cell Envelope and Interactions with Antimicrobials: Insights from Molecular Dynamics Simulations

Bacteria have evolved over 3 billion years, shaping our intrinsic and symbiotic coexistence with these single-celled organisms. With rising populations of drug-resistant strains, the search for novel antimicrobials is an ongoing area of research. Advances in high-performance computing platforms have led to a variety of molecular dynamics simulation strategies to study the interactions of antimicrobial molecules with different compartments of the bacterial cell envelope of both Gram-positive and Gram-negative species. In this review, we begin with a detailed description of the structural aspects of the bacterial cell envelope. Simulations concerned with the transport and associated free energy of small molecules and ions through the outer membrane, peptidoglycan, inner membrane and outer membrane porins are discussed. Since surfactants are widely used as antimicrobials, a section is devoted to the interactions of surfactants with the cell wall and inner membranes. The review ends with a discussion on antimicrobial peptides and the insights gained from the molecular simulations on the free energy of translocation. Challenges involved in developing accurate molecular models and coarse-grained strategies that provide a trade-off between atomic details with a gain in sampling time are highlighted. The need for efficient sampling strategies to obtain accurate free energies of translocation is also discussed. Molecular dynamics simulations have evolved as a powerful tool that can potentially be used to design and develop novel antimicrobials and strategies to effectively treat bacterial infections.

Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications

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

Antimicrobial peptides: An alternative to traditional antibiotics

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

Synergistic interaction of polymyxin B with carvacrol: antimicrobial strategy against polymyxin-resistant Klebsiella pneumoniae

Objective: The antimicrobial activities of the synergistic combination of carvacrol and polymyxin B against polymyxin-resistant Klebsiella pneumoniae were evaluated. Methods: The methods employed checkerboard assays to investigate synergism, biofilm inhibition assessment and membrane integrity assay. In addition, the study included in vivo evaluation using a mouse infection model. Results: The checkerboard method evaluated 48 combinations, with 23 indicating synergistic action. Among these, carvacrol 10 mg/kg plus polymyxin B 2 mg/kg exhibited in vivo antimicrobial activity in a mouse model of infection, resulting in increased survival and a significant decrease in bacterial load in the blood. Conclusion: Polymyxin in synergy with carvacrol represents a promising alternative to be explored in the development of new antimicrobials.

Structure, Function, and Physicochemical Properties of Pore-forming Antimicrobial Peptides

Antimicrobial peptides (AMPs), a class of antimicrobial agents, possess considerable potential to treat various microbial ailments. The broad range of activity and rare complete bacterial resistance to AMPs make them ideal candidates for commercial development. These peptides with widely varying compositions and sources share recurrent structural and functional features in mechanisms of action. Studying the mechanisms of AMP activity against bacteria may lead to the development of new antimicrobial agents that are more potent. Generally, AMPs are effective against bacteria by forming pores or disrupting membrane barriers. The important structural aspects of cytoplasmic membranes of pathogens and host cells will also be outlined to understand the selective antimicrobial actions. The antimicrobial activities of AMPs are related to multiple physicochemical properties, such as length, sequence, helicity, charge, hydrophobicity, amphipathicity, polar angle, and also self-association. These parameters are interrelated and need to be considered in combination. So, gathering the most relevant available information will help to design and choose the most effective AMPs.

Peptide Power: Mechanistic Insights into the Effect of Mitochondria-Targeted Tetrapeptides on Membrane Electrostatics from Molecular Simulations

Mitochondrial dysfunction is implicated in nine of the ten leading causes of death in the US, yet there are no FDA-approved therapeutics to treat it. Synthetic mitochondria-targeted peptides (MTPs), including the lead compound SS-31, offer promise, as they have been shown to restore healthy mitochondrial function and treat a variety of common diseases. At the cellular level, research has shown that MTPs accumulate strongly at the inner mitochondrial membrane (IMM), slow energy sinks (e.g., proton leaks), and improve ATP production. Modulation of electrostatic fields around the IMM has been implicated as a key aspect in the mechanism of action (MoA) of these peptides; however, molecular and mechanistic details have remained elusive. In this study, we employed all-atom molecular dynamics simulations (MD) to investigate the interactions of four MTPs with lipid bilayers and calculate their effect on structural and electrostatic properties. In agreement with previous experimental findings, we observed the modulation of the membrane surface and dipole potentials by MTPs. The simulations reveal that the MTPs achieve a reduction in the dipole potential by acting to disorder both lipid head groups and water layers proximal to the bilayer surface. We also find that MTPs decrease the bilayer thickness and increase the membrane's capacitance. These changes suggest that MTPs may enhance how much potential energy can be stored across the IMM at a given transmembrane potential difference. The MTPs also displace cations away from the bilayer surface, modulating the surface potential and offering an alternative mechanism for how these MTPs reduce mitochondrial energy sinks like proton leaks and mitigate Ca2+ accumulation stress. In conclusion, this study highlights the therapeutic potential of MTPs and underlines how interactions of MTPs with lipid bilayers serve as a fundamental component of their MoA.

Artificial intelligence-driven antimicrobial peptide discovery

Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.

Progress towards the clinical use of antimicrobial peptides: challenges and opportunities

INTRODUCTION

To overcome the challenge of multidrug resistance, natural and synthetic peptides are candidates to become the basis of innovative therapeutics, featuring diverse mechanisms of action. Traditionally, the time elapsed from medical discoveries to their application is long. The urgency derived from the emergence of antibiotic resistance recommends an acceleration of research to put the new weapons in the hands of clinicians.

AREAS COVERED

This narrative review introduces ideas and suggestions of new strategies that may be used as a basis upon which to recommend reduced development times and to facilitate the arrival of new molecules in the fight against microbes.

EXPERT OPINION

Although studies on new innovative antimicrobial treatments are being conducted, sooner rather than later, more clinical trials, preclinical and translational research are needed to promote the development of innovative antimicrobial treatments for multidrug resistant infections. The situation is worrying, no less than that generated by pandemics such as the ones we have just experienced and conflicts such as world wars. Although from the point of view of human perception, resistance to antibiotics may not seem as serious as these other situations, it is possibly the hidden pandemic that most jeopardizes the future of medicine.

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.

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.

Relating Molecular Dynamics Simulations to Functional Activity for Gly-Rich Membranolytic Helical Kiadin Peptides

Kiadins are in silico designed peptides with a strong similarity to diPGLa-H, a tandem sequence of PGLa-H (KIAKVALKAL) and with single, double or quadruple glycine substitutions. They were found to show high variability in their activity and selectivity against Gram-negative and Gram-positive bacteria, as well as cytotoxicity against host cells, which are influenced by the number and placing of glycine residues along the sequence. The conformational flexibility introduced by these substitutions contributes differently peptide structuring and to their interactions with the model membranes, as observed by molecular dynamics simulations. We relate these results to experimentally determined data on the structure of kiadins and their interactions with liposomes having a phospholipid membrane composition similar to simulation membrane models, as well as to their antibacterial and cytotoxic activities, and also discuss the challenges in interpreting these multiscale experiments and understanding why the presence of glycine residues in the sequence affected the antibacterial potency and toxicity towards host cells in a different manner.

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.

Krein support vector machine classification of antimicrobial peptides

Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.

CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications

Molecular dynamics simulations of membranes and membrane proteins serve as computational microscopes, revealing coordinated events at the membrane interface. As G protein-coupled receptors, ion channels, transporters, and membrane-bound enzymes are important drug targets, understanding their drug binding and action mechanisms in a realistic membrane becomes critical. Advances in materials science and physical chemistry further demand an atomistic understanding of lipid domains and interactions between materials and membranes. Despite a wide range of membrane simulation studies, generating a complex membrane assembly remains challenging. Here, we review the capability of CHARMM-GUI Membrane Builder in the context of emerging research demands, as well as the application examples from the CHARMM-GUI user community, including membrane biophysics, membrane protein drug-binding and dynamics, protein-lipid interactions, and nano-bio interface. We also provide our perspective on future Membrane Builder development.

Advances of Antimicrobial Peptide-Based Biomaterials for the Treatment of Bacterial Infections

Owing to the increase in multidrug-resistant bacterial isolates in hospitals globally and the lack of truly effective antimicrobial agents, antibiotic resistant bacterial infections have increased substantially. There is thus an urgent need to develop new antimicrobial drugs and their related formulations. In recent years, natural antimicrobial peptides (AMPs), AMP optimization, self-assembled AMPs, AMP hydrogels, and biomaterial-assisted delivery of AMPs have shown great potential in the treatment of bacterial infections. In this review, it is focused on the development prospects and shortcomings of various AMP-based biomaterials for treating animal model infections, such as abdominal, skin, and eye infections. It is hoped that this review will inspire further innovations in the design of AMP-based biomaterials for the treatment of bacterial infections and accelerate their commercialization.

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.

Helminthic host defense peptides: using the parasite to defend the host

Parasitic helminths are destined to share niches with a variety of microbiota that inevitably influence their interaction with the host. To modulate the microbiome for their benefit and defend against pathogenic isolates, helminths have developed host defense peptides (HDPs) and proteins as integral elements of their immunity. These often exert a relatively nonspecific membranolytic activity toward bacteria, sometimes with limited or no toxicity toward host cells. With a few exceptions, such as nematode cecropin-like peptides and antibacterial factors (ABFs), helminthic HDPs are largely underexplored. This review scrutinizes current knowledge on the repertoire of such peptides in helminths and promotes their research as potential leads for an anti-infective solution to the burgeoning problem of antibiotic resistance.

Peptidomics analysis of Jiang-Flavor Daqu from high-temperature fermentation to mature and in different preparation season

Jiang-Flavor Daqu (JFDQ) is a grain-type fermented starter for brewing Chinese liquor. Peptides, the metabolites of proteins in JFDQ, are important for the quality and flavor of JFDQ or even the liquor. The peptide variations in the progress of JFDQ preparation were investigated using RPLC-MS/MS. The JFDQ after high-temperature fermenting (HTF_SU) and after ripening (M_SU), as well as the mature JFDQ prepared in spring (M_SP) and in summer (M_SU), were compared respectively. These two groups were investigated from peptides, precursor proteins, abundance, interactions, and potential antimicrobial peptides (pAMPs). A total of 177, 158, and 262 peptides from HTF_SU, M_SP, and M_SU were identified, respectively. Significant differences (P < 0.01) in the abundance of shared peptides were found in different fermentation stage group (HTF_M), and stronger positive correlations were observed in different preparation season group (MSP_MSU). The interactions of the shared peptides in HTF_M and in MSP_MSU were investigated respectively. In addition, 8 pAMPs in HTF_SU, 5 in M_SP, and 22 in M_SU were predicted using CAMPR3, and their core functional regions were analyzed. This systematic study demonstrated the influences of fermentation stage and preparation season on the peptide profiles in JFDQ, which would provide theoretical guidance and be helpful for JFDQ production. SIGNIFICANCE: Peptidomics analysis showed that the peptide profiles of JFDQ varied in different fermentation stages and different preparation seasons, which mainly resulted from the peptides with high abundance, high interaction degrees, and potential antimicrobial activity, as well as the important precursor proteins such as glutens. This systematic study would benefit for the insufficiency of peptide research of JFDQ till now, and provide theoretical guidance for JFDQ production.

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ∼12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the de novo design and in silico evaluation of signal peptides.

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.

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.

Multidrug-Resistant Microbial Therapy Using Antimicrobial Peptides and the CRISPR/Cas9 System

The emergence and spread of multidrug-resistant microbes become a serious threat to animal and human health globally because of their less responsiveness to conventional antimicrobial therapy. Multidrug-resistant microbial infection poses higher morbidity and mortality rate with significant economic losses. Currently, antimicrobial peptides and the CRISPR/Cas9 system are explored as alternative therapy to circumvent the challenges of multidrug-resistant organisms. Antimicrobial peptides are small molecular weight, cationic peptides extracted from all living organisms. It is a promising drug candidate for the treatment of multidrug-resistant microbes by direct microbial killing or indirectly modulating the innate immune system. The CRISPR/Cas9 system is another novel antimicrobial alternative used to manage multidrug-resistant microbial infection. It is a versatile gene-editing tool that uses engineered single guide RNA for targeted gene recognition and the Cas9 enzyme for the destruction of target nucleic acids. Both the CRISPR/Cas9 system and antimicrobial peptides were used to successfully treat nosocomial infections caused by ESKAPE pathogens, which developed resistance to various antimicrobials. Despite, their valuable roles in multidrug-resistant microbial treatments, both the antimicrobial peptides and the CRISPR/Cas systems have various limitations like toxicity, instability, and incurring high manufacturing costs. Thus, this review paper gives detailed explanations of the roles of the CRISPR/Cas9 system and antimicrobial peptides in circumventing the challenges of multidrug-resistant microbial infections, its limitation and prospects in clinical applications.

Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides

Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α‐helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non‐hemolytic from hemolytic AMPs and ACPs to discover new non‐hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty‐three peptides resulted in eleven active ACPs, four of which were non‐hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non‐hemolytic ACPs.

Antibacterial Peptide NP-6 Affects Staphylococcus aureus by Multiple Modes of Action

Our previous study extracted and identified an antibacterial peptide that was named NP-6. Herein, we investigated the physicochemical properties of NP-6, and elucidated the mechanisms underlying its antimicrobial activity against Staphylococcus aureus. The results showed that the hemolysis activity of NP-6 was 2.39 ± 0.13%, lower than Nisin A (3.91 ± 0.43%) at the same concentration (512 µg/mL). Negligible cytotoxicity towards RAW264.7 cells was found when the concentration of NP-6 was lower than 512 µg/mL. In addition, it could keep most of its activity in fetal bovine serum. Moreover, transmission electron microscopy, confocal laser scanning microscopy, and flow cytometry results showed that NP-6 can destroy the integrity of the bacterial cell membrane and increase the membrane permeability. Meanwhile, NP-6 had binding activity with bacterial DNA and RNA in vitro and strongly inhibited the intracellular β-galactosidase activity of S. aureus. Our findings suggest that NP-6 could be a promising candidate against S. aureus.

Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage

Antibiotic resistance is a global health crisis increasing in prevalence every day. To combat this crisis, alternative antimicrobial therapeutics are urgently needed. Antimicrobial peptides (AMPs), a family of short defense proteins, are produced naturally by all organisms and hold great potential as effective alternatives to small molecule antibiotics. Here, we present rAMPage, a scalable bioinformatics discovery platform for identifying AMP sequences from RNA sequencing (RNA-seq) datasets. In our study, we demonstrate the utility and scalability of rAMPage, running it on 84 publicly available RNA-seq datasets from 75 amphibian and insect species—species known to have rich AMP repertoires. Across these datasets, we identified 1137 putative AMPs, 1024 of which were deemed novel by a homology search in cataloged AMPs in public databases. We selected 21 peptide sequences from this set for antimicrobial susceptibility testing against Escherichia coli and Staphylococcus aureus and observed that seven of them have high antimicrobial activity. Our study illustrates how in silico methods such as rAMPage can enable the fast and efficient discovery of novel antimicrobial peptides as an effective first step in the strenuous process of antimicrobial drug development.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides

The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme. While the peptide physicochemical and compositional representations fail to distinguish sequence permutations, the amino acid arrangement within the sequence lacks the important information contained in physicochemical, conformational, topological, and geometrical properties. In this paper, we propose a solution to the identified information gap by implementing a hybrid scheme that complements the best traits from both approaches with the aim of predicting antimicrobial and antiviral activities based on experimental data from DRAMP 2.0, AVPdb, and Uniprot data repositories. Using the Friedman test of statistical significance, we compared our hybrid, sequential properties approach to peptide properties, one-hot vector encoding, and word embedding schemes in the 10-fold cross-validation setting, with respect to the F1 score, Matthews correlation coefficient, geometric mean, recall, and precision evaluation metrics. Moreover, the sequence modeling neural network was employed to gain insight into the synergic effect of both properties- and amino acid order-based predictions. The results suggest that sequential properties significantly (P < 0.01) surpasses the aforementioned state-of-the-art representation schemes. This makes it a strong candidate for increasing the predictive power of screening methods based on machine learning, applicable to any category of peptides.

Atomistic Simulation of Molecules Interacting with Biological Nanopores: From Current Understanding to Future Directions

Biological nanopores have been at the focus of numerous studies due to their role in many biological processes as well as their (prospective) technological applications. Among many other topics, recent studies on nanopores have addressed two key areas: antibiotic permeation through bacterial channels and sensing of analytes. Although the two areas are quite far apart in terms of their objectives, in both cases atomistic simulations attempt to understand the solute dynamics and the solute-protein interactions within the channel lumen. While decades of studies on various channels have culminated in an improved understanding of the key molecular factors and led to practical applications in some cases, successful utilization is limited. In this Perspective we summarize recent progress in understanding key issues in molecular simulations of antibiotic translocation and in the development of nanopore sensors. Moreover, we comment on possible advancements in computational algorithms that can potentially resolve some of the issues.

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.

Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain

Specific interaction between the postsynaptic density protein 95 (PSD95) and synapse-associated protein 90/postsynaptic density 95–associated protein (SAPAP) is crucial for excitatory synaptic development and plasticity. Designing inhibitors that target the guanylate kinase (GK) domain of PSD95, which is responsible for the interaction, is a promising manipulation tool for the investigation of the function of PSD95 GK and the etiology of its related psychiatric disorders. Herein, we designed new peptide inhibitors of PSD95 GK/SAPAP with higher binding affinity by using molecular dynamics simulations. First, the interactions between PSD95 GK and their reported phosphorylated and unphosphorylated peptides were explored by molecular dynamics simulations. Besides the hydrogen bonding interactions mediated by the phospho-serine (p-Ser) or corresponding phosphomimic residue Asp/Glu, the hydrophobic interactions from the other amino acids also contribute to the PSD95 GK/SAPAP interaction. As an unphosphorylated synthetic peptide with moderate binding affinity and relatively lower molecular weight, the QSF inhibitory peptide was selected for further modification. Based on per-residue energy decomposition results of the PSD95 GK/QSF complex, ten peptides were designed to enhance the binding interactions, especially the hydrophobic interactions. The top-ranked five peptides with lower binding energy were eventually synthesized. The binding affinities of the synthesized peptides were determined using fluorescence polarization (FP) assay. As expected, all peptides have higher binding affinity than the QSF peptide (Ki = 5.64 ± 0.51 μM). Among them, F10W was the most potent inhibitor (Ki = 0.75 ± 0.25 μM), suggesting that enhancement of the hydrophobic interactions is an important strategy for the design of new inhibitory peptides targeting PSD95 GK.

Multiple Antimicrobial Effects of Hybrid Peptides Synthesized Based on the Sequence of Ribosomal S1 Protein from Staphylococcus aureus

The need to develop new antimicrobial peptides is due to the high resistance of pathogenic bacteria to traditional antibiotics now and in the future. The creation of synthetic peptide constructs is a common and successful approach to the development of new antimicrobial peptides. In this work, we use a simple, flexible, and scalable technique to create hybrid antimicrobial peptides containing amyloidogenic regions of the ribosomal S1 protein from Staphylococcus aureus. While the cell-penetrating peptide allows the peptide to enter the bacterial cell, the amyloidogenic site provides an antimicrobial effect by coaggregating with functional bacterial proteins. We have demonstrated the antimicrobial effects of the R23F, R23DI, and R23EI hybrid peptides against Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), Pseudomonas aeruginosa, Escherichia coli, and Bacillus cereus. R23F, R23DI, and R23EI can be used as antimicrobial peptides against Gram-positive and Gram-negative bacteria resistant to traditional antibiotics.

Structure-activity trend analysis between amino-acids and minimal inhibitory concentration of antimicrobial peptides

Antimicrobial peptides (AMPs) provide large structural libraries of molecules with high variability of constitutional amino-acids (AAs). Highlighting structural organization and structure-activity trends in such molecular systems provide key information on structural associations and functional conditions that could usefully help for drug design. This work presents link analyses between minimal inhibitory concentration (MIC) and different types of constitutional AAs of anti-Pseudomonas aeruginosa AMPs. This scope was based on a dataset of 328 published molecules. Regulation levels of AAs in AMPs were statistically ordinated by correspondence analysis helping for classification of the 328 AMPs into nine structurally homogeneous peptide clusters (PCs 1-9) characterized by high/low relative occurrences of different AAs. Within each PC, negative trends between MIC and AAs were highlighted by iterated multiple linear regression models built by bootstrap processes (bagging). MIC-decrease was linked to different AAs that varied with PCs: alcohol type AAs (Thr, Ser) in Cys-rich and low Arg PCs (PCs 1-3); basic AAs (Lys, Arg) in Pro-rich and low Val PCs (PCs 4-8); Trp (heterocyclic AA) in Arg-rich PCs (PCs 6, 7, 9). Aliphatic AAs (more particularly Gly) showed MIC-reduction effects in different PCs essentially under interactive forms.

Agonist/Antagonist Activity of Oxytocin Variants Obtained from Free Cyclic Peptide Libraries Generated via Amino Acid Substitution

We established a method for synthesizing a free cyclic peptide library via peptide array synthesis to demonstrate the sequence activity of cyclic peptides. Variants of the cyclic nonapeptide oxytocin (OXT) were synthesized via residue substitution. Natural amino acids (AAs) were classified into eight groups based on their physical properties and the size of their side chains, and a representative AA from each group was selected for residue substitution. All OXT variants were systematically evaluated for agonist/antagonist activity. Consequently, no improvement in agonist activity was observed, although substitution of the P4 and P8 residues resulted in decreased activity due to AA substitution. A few OXT variants exhibited antagonistic activity. In particular, the variants with P2 Leu residue substitution (Y2L) and Phe substitutions at residues 4 (Q4F), 5 (N5F), and 7 (P7F) showed high antagonistic activity. Variant Y2W was found to have the highest inhibitory effect, with a dissociation constant of 44 nM, which was comparable to that of the commercial antagonist atosiban (21 nM). Therefore, a free cyclic peptide library constructed via substitution with a natural AA residue was confirmed to be a powerful tool for bioactive peptide screening.

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.

Modeling the Structure of Crystalline Alamethicin and Its NMR Chemical Shift Tensors

Alamethicin (ALM) is an antimicrobial peptide that is frequently employed in studies of the mechanism of action of pore-forming molecules. Advanced techniques of solid-state NMR spectroscopy (SSNMR) are important in these studies, as they are capable of describing the alignment of helical peptides, such as ALM, in lipid bilayers. Here, it is demonstrated how an analysis of the SSNMR measurements can benefit from fully periodic calculations, which employ the plane-wave density-functional theory (PW DFT) of the solid-phase geometry and related spectral parameters of ALM. The PW DFT calculations are used to obtain the structure of desolvated crystalline ALM and predict the NMR chemical shift tensors (CSTs) of its nuclei. A variation in the CSTs of the amidic nitrogens and carbonyl carbons along the ALM backbone is evaluated and included in simulations of the orientation-dependent anisotropic 15N and 13C chemical shift components. In this way, the influence of the site-specific structural effects on the experimentally determined orientation of ALM is shown in models of cell membranes.

Discovery, Optimization, and Clinical Application of Natural Antimicrobial Peptides

Antimicrobial peptides (AMPs) are widespread in multicellular organisms. These structurally diverse molecules are produced as the first line of defense against pathogens such as bacteria, viruses, fungi, and parasites. Also known as host defense peptides in higher eukaryotic organisms, AMPs display immunomodulatory and anticancer activities. During the last 30 years, technological advances have boosted the research on antimicrobial peptides, which have also attracted great interest as an alternative to tackling the antimicrobial resistance scenario mainly provoked by some bacterial and fungal pathogens. However, the introduction of natural AMPs in clinical trials faces challenges such as proteolytic digestion, short half-lives, and cytotoxicity upon systemic and oral application. Therefore, some strategies have been implemented to improve the properties of AMPs aiming to be used as effective therapeutic agents. In the present review, we summarize the discovery path of AMPs, focusing on preclinical development, recent advances in chemical optimization and peptide delivery systems, and their introduction into the market.