Simulation-Guided Rational de Novo Design of a Small Pore-Forming Antimicrobial Peptide

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides

Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents is of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated via an active machine learning approach. The approach identified 71 potential PFPs from the 25.6 billion octapeptide sequence space; 13 sequences were tested experimentally, and all were found to have the predicted membrane-disrupting ability, with 1 forming highly stable pores. Experimental verification of the predicted pore-forming ability demonstrated that a range of short peptides can form pores in membranes, while the positioning and characteristics of residues that favour pore-forming behaviour were identified. This approach identified more ultrashort (8-residues, unmodified, non-cyclic) PFPs than previously known. We anticipate our findings and methodology will be useful in discovering new pore-forming and membrane-disrupting peptides for a range of applications from nanoreactors to therapeutics.

Structural Determinants of Peptide Nanopore Formation

We have evolved the nanopore-forming macrolittin peptides from the bee venom peptide melittin using successive generations of synthetic molecular evolution. Despite their sequence similarity to the broadly membrane permeabilizing cytolytic melittin, the macrolittins have potent membrane selectivity. They form nanopores in synthetic bilayers made from 1-palmitoyl, 2-oleoyl-phosphatidylcholine (POPC) at extremely low peptide concentrations and yet have essentially no cytolytic activity against any cell membrane, even at high concentration. Here, we explore the structural determinants of macrolittin nanopore stability in POPC bilayers using atomistic molecular dynamics simulations and experiments on macrolittins and single-site variants. Simulations of macrolittin nanopores in POPC bilayers show that they are stabilized by an extensive, cooperative hydrogen bond network comprised of the many charged and polar side chains interacting with each other via bridges of water molecules and lipid headgroups. Lipid molecules with unusual conformations participate in the H-bond network and are an integral part of the nanopore structure. To explore the role of this H-bond network on membrane selectivity, we swapped three critical polar residues with the nonpolar residues found in melittin. All variants have potency, membrane selectivity, and cytotoxicity that were intermediate between a cytotoxic melittin variant called MelP5 and the macrolittins. Simulations showed that the variants had less organized H-bond networks of waters and lipids with unusual structures. The membrane-spanning, cooperative H-bond network is a critical determinant of macrolittin nanopore stability and membrane selectivity. The results described here will help guide the future design and optimization of peptide nanopore-based applications.

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

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

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

Antibacterial and Antiviral Properties of Chenopodin-Derived Synthetic Peptides

Antimicrobial peptides have been developed based on plant-derived molecular scaffolds for the treatment of infectious diseases. Chenopodin is an abundant seed storage protein in quinoa, an Andean plant with high nutritional and therapeutic properties. Here, we used computer- and physicochemical-based strategies and designed four peptides derived from the primary structure of Chenopodin. Two peptides reproduce natural fragments of 14 amino acids from Chenopodin, named Chen1 and Chen2, and two engineered peptides of the same length were designed based on the Chen1 sequence. The two amino acids of Chen1 containing amide side chains were replaced by arginine (ChenR) or tryptophan (ChenW) to generate engineered cationic and hydrophobic peptides. The evaluation of these 14-mer peptides on Staphylococcus aureus and Escherichia coli showed that Chen1 does not have antibacterial activity up to 512 µM against these strains, while other peptides exhibited antibacterial effects at lower concentrations. The chemical substitutions of glutamine and asparagine by amino acids with cationic or aromatic side chains significantly favoured their antibacterial effects. These peptides did not show significant hemolytic activity. The fluorescence microscopy analysis highlighted the membranolytic nature of Chenopodin-derived peptides. Using molecular dynamic simulations, we found that a pore is formed when multiple peptides are assembled in the membrane. Whereas, some of them form secondary structures when interacting with the membrane, allowing water translocations during the simulations. Finally, Chen2 and ChenR significantly reduced SARS-CoV-2 infection. These findings demonstrate that Chenopodin is a highly useful template for the design, engineering, and manufacturing of non-toxic, antibacterial, and antiviral peptides.

A systematical review on antimicrobial peptides and their food applications

Food safety issues are a major concern in food processing and packaging industries. Food spoilage is caused by microbial contamination, where antimicrobial peptides (APs) provide solutions by eliminating microorganisms. APs such as nisin have been successfully and commonly used in food processing and preservation. Here, we discuss all aspects of the functionalization of APs in food applications. We briefly review the natural sources of APs and their native functions. Recombinant expression of APs in microorganisms and their yields are described. The molecular mechanisms of AP antibacterial action are explained, and this knowledge can further benefit the design of functional APs. We highlight current utilities and challenges for the application of APs in the food industry, and address rational methods for AP design that may overcome current limitations.

Design and evaluation of tadpole-like conformational antimicrobial peptides

Antimicrobial peptides are promising alternatives to conventional antibiotics. Herein, we report a class of "tadpole-like" peptides consisting of an amphipathic α-helical head and an aromatic tail. A structure-activity relationship (SAR) study of "tadpole-like" temporin-SHf and its analogs revealed that increasing the number of aromatic residues in the tail, introducing Arg to the α-helical head and rearranging the peptide topology dramatically increased antimicrobial activity. Through progressive structural optimization, we obtained two peptides, HT2 and RI-HT2, which exhibited potent antimicrobial activity, no hemolytic activity and cytotoxicity, and no propensity to induce resistance. NMR and molecular dynamics simulations revealed that both peptides indeed adopted "tadpole-like" conformations. Fluorescence experiments and electron microscopy confirmed the membrane targeting mechanisms of the peptides. Our studies not only lead to the discovery of a series of ultrashort peptides with potent broad-spectrum antimicrobial activities, but also provide a new strategy for rational design of novel "tadpole-like" antimicrobial peptides.

Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations

Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1β and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1β inhibition. However, NLRC4 and AIM2 inflammasome-mediated IL-1β secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.

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.

How do antimicrobial peptides disrupt the lipopolysaccharide membrane leaflet of Gram-negative bacteria?

HYPOTHESIS

It is widely regarded that antimicrobial peptides (AMPs) kill bacteria by physically disrupting microbial membranes and causing cytoplasmic leakage, but it remains unclear how AMPs disrupt the outer membrane (OM) of Gram-negative bacteria (GNB) and then compromise the inner membrane. We hypothesise that different AMPs impose different structural disruptions, with direct implications to their antimicrobial efficacies.

EXPERIMENTS

The antimicrobial activities of three typical AMPs, including the designed short AMP, G₃, and two natural AMPs, melittin and LL37, against E. coli and their haemolytic activities were studied. Lipopolysaccharide (LPS) and anionic di-palmitoyl phosphatidyl glycerol (DPPG) monolayer models were constructed to mimic the outer membrane and inner membrane leaflets of Gram-negative bacteria. The binding and penetration of AMPs to the model lipid monolayers were systematically studied by neutron reflection via multiple H/D contrast variations.

FINDING

G₃ has relatively high antimicrobial activity, low cytotoxicity, and high proteolytic stability, whilst melittin has significant haemolysis and LL37 has weaker antimicrobial activity. G₃ could rapidly lyse LPS and DPPG monolayers within 10-20 min. In contrast, melittin was highly active against the LPS membrane, but the dynamic process lasted up to 80 min, with excessive stacking in the OM. LL37 caused rather weak destruction to LPS and DPPG monolayers, leading to massive adsorption on the membrane surface without penetrating the lipid tail region. These findings demonstrate that the rationally designed AMP G₃ was well optimised to impose most effective destruction to bacterial membranes, consistent with its highest bactericidal activity. These different interfacial structural features associated with AMP binding shed light on the future development of active and biocompatible AMPs for infection and wound treatments.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

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

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

Intramembrane Nanoaggregates of Antimicrobial Peptides Play a Vital Role in Bacterial Killing

Recent developments in antimicrobial peptides (AMPs) have focused on the rational design of short sequences with less than 20 amino acids due to their relatively low synthesis costs and ease of correlation of the structure-function relationship. However, gaps remain in the understanding of how short cationic AMPs interact with the bacterial outer and inner membranes to affect their antimicrobial efficacy and dynamic killing. The membrane-lytic actions of two designed AMPs, G(IIKK)₃ I-NH₂ (G₃ ) and G(IIKK)₄ I-NH₂ (G₄ ), and previously-studied controls GLLDLLKLLLKAAG-NH₂ (LDKA, biomimetic) and GIGAVLKVLTTGLPALISWIKRKR-NH₂ (Melittin, natural) are examined. The mechanistic processes of membrane damage and the disruption strength of the four AMPs are characterized by molecular dynamics simulations and experimental measurements including neutron reflection and scattering. The results from the combined studies are characterized with distinctly different intramembrane nanoaggregates formed upon AMP-specific binding, reflecting clear influences of AMP sequence, charge and the chemistry of the inner and outer membranes. G₃ and G₄ display different nanoaggregation with the outer and inner membranes, and the smaller sizes and further extent of insertion of the intramembrane nanoaggregates into bacterial membranes correlate well with their greater antimicrobial efficacy and faster dynamic killing. This work demonstrates the crucial roles of intramembrane nanoaggregates in optimizing antimicrobial efficacy and dynamic killing.

An optimized antimicrobial peptide analog acts as an antibiotic adjuvant to reverse methicillin-resistant Staphylococcus aureus

The misuse of antibiotics in animal protein production has driven the emergence of a range of drug-resistant pathogens, which threaten existing public health security. Consequently, there is an urgent need to develop novel antimicrobials and new infection treatment options to address the challenges posed by the dramatic spread of antibiotic resistance. Piscidins, a class of fish-specific antimicrobial peptides (AMPs), are regarded as promising therapies for biomedical applications. Progress towards potential analogs from the piscidin family has been hampered by unenforceable structural optimization strategies. Here, we leverage a strategy of bioinformatics analysis combined with molecular dynamics (MD) simulation to identify specific functional hotspots in piscidins and rationally design related analogues. As expected, this approach yields a potent and non-toxic PIS-A-1 that can be used as an antibiotic adjuvant to reverse methicillin-resistant Staphylococcus aureus (MRSA) pathogens. Remarkably, the structural optimization scheme and application strategy proposed here will contribute richer therapeutic options for the safe production of animal protein.

Matching amino acids membrane preference profile to improve activity of antimicrobial peptides

Antimicrobial peptides (AMPs) are cationic antibiotics that can kill multidrug-resistant bacteria via membrane insertion. However, their weak activity limits their clinical use. Ironically, the cationic charge of AMPs is essential for membrane binding, but it obstructs membrane insertion. In this study, we postulate that this problem can be overcome by locating cationic amino acids at the energetically preferred membrane surface. All amino acids have an energetically preferred or less preferred membrane position profile, and this profile is strongly related to membrane insertion. However, most AMPs do not follow this profile. One exception is protegrin-1, a powerful but neglected AMP. In the present study, we found that a potent AMP, WCopW5, strongly resembles protegrin-1 and that the match between its sequence and the preferred position profile closely correlates with its antimicrobial activity. One of its derivatives, WCopW43, has antimicrobial activity comparable to that of the most effective AMPs in clinical use.

Ornamental tobacco floral nectar is a rich source of antimicrobial peptides

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

Alternative Antibiotics in Dentistry: Antimicrobial Peptides

The rise of antibiotic resistant bacteria due to overuse and misuse of antibiotics in medicine and dentistry is a growing concern. New approaches are needed to combat antibiotic resistant (AR) bacterial infections. There are a number of methods available and in development to address AR infections. Dentists conventionally use chemicals such as chlorohexidine and calcium hydroxide to kill oral bacteria, with many groups recently developing more biocompatible antimicrobial peptides (AMPs) for use in the oral cavity. AMPs are promising candidates in the treatment of (oral) infections. Also known as host defense peptides, AMPs have been isolated from animals across all kingdoms of life and play an integral role in the innate immunity of both prokaryotic and eukaryotic organisms by responding to pathogens. Despite progress over the last four decades, there are only a few AMPs approved for clinical use. This review summarizes an Introduction to Oral Microbiome and Oral Infections, Traditional Antibiotics and Alternatives & Antimicrobial Peptides. There is a focus on cationic AMP characteristics and mechanisms of actions, and an overview of animal-derived natural and synthetic AMPs, as well as observed microbial resistance.

Highly selective performance of rationally designed antimicrobial peptides based on ponericin-W1

Antimicrobial peptides (AMPs) or host-defence peptides act by penetrating and disrupting the bacterial membranes and are therefore less prone to antimicrobial resistance (AMR) compared to conventional antibiotics. However, there are still many challenges in the clinical application of the naturally occurring AMPs which necessitates further studies to establish the relationship between the chemical structure of AMPs and their antimicrobial activity and selectivity. Herein, we report a study on the relationship between the chemical structure and the biological activity of a series of rationally designed AMPs derived from Ponericin-W1, a naturally occurring AMP from ants. The peptides were designed by modification of the hydrophobic and hydrophilic regions of the lead peptide sequence in a systematic way. Their antibacterial and hemolytic activities were determined in vitro. The antibacterial activity of a representative peptide, At5 was also tested in a mouse model of skin wound infection. Furthermore, the relationship between the physicochemical properties of the peptides and their antibacterial activity was investigated. Replacing the cationic amino acids in the hydrophobic region of the peptides with hydrophobic amino acids enhanced their antibacterial activity and increasing the number of cationic amino acids in the hydrophilic region reduced their toxicity to human red blood cells and thus improved their selectivity for bacteria. Four of the designed peptides, coded as At3, At5, At8, and At10, displayed considerable antibacterial activity and high selectivity for bacteria. At5 also accelerated the wound healing in mice indicating high in vivo efficiency of this peptide. The peptides were more effective against Gram-negative bacteria and no AMR was developed against them in the bacteria even after 25 generations. The results from this study can provide a better understanding of the structural features required for strong antibacterial activity and selectivity, and serve as a guide for the future rational design of AMPs.

The Multistage Antimalarial Compound Calxinin Perturbates P. falciparum Ca2+ Homeostasis by Targeting a Unique Ion Channel

Malaria elimination urgently needs novel antimalarial therapies that transcend resistance, toxicity, and high costs. Our multicentric international collaborative team focuses on developing multistage antimalarials that exhibit novel mechanisms of action. Here, we describe the design, synthesis, and evaluation of a novel multistage antimalarial compound, ‘Calxinin’. A compound that consists of hydroxyethylamine (HEA) and trifluoromethyl-benzyl-piperazine. Calxinin exhibits potent inhibitory activity in the nanomolar range against the asexual blood stages of drug-sensitive (3D7), multidrug-resistant (Dd2), artemisinin-resistant (IPC4912), and fresh Kenyan field isolated Plasmodium falciparum strains. Calxinin treatment resulted in diminished maturation of parasite sexual precursor cells (gametocytes) accompanied by distorted parasite morphology. Further, in vitro liver-stage testing with a mouse model showed reduced parasite load at an IC₅₀ of 79 nM. A single dose (10 mg/kg) of Calxinin resulted in a 30% reduction in parasitemia in mice infected with a chloroquine-resistant strain of the rodent parasite P. berghei. The ex vivo ookinete inhibitory concentration within mosquito gut IC₅₀ was 150 nM. Cellular in vitro toxicity assays in the primary and immortalized human cell lines did not show cytotoxicity. A computational protein target identification pipeline identified a putative P. falciparum membrane protein (Pf3D7_1313500) involved in parasite calcium (Ca²⁺) homeostasis as a potential Calxinin target. This highly conserved protein is related to the family of transient receptor potential cation channels (TRP-ML). Target validation experiments showed that exposure of parasitized RBCs (pRBCs) to Calxinin induces a rapid release of intracellular Ca²⁺ from pRBCs; leaving de-calcinated parasites trapped in RBCs. Overall, we demonstrated that Calxinin is a promising antimalarial lead compound with a novel mechanism of action and with potential therapeutic, prophylactic, and transmission-blocking properties against parasites resistant to current antimalarials.

Integrated Design of a Membrane-Lytic Peptide-Based Intravenous Nanotherapeutic Suppresses Triple-Negative Breast Cancer

Membrane-lytic peptides offer broad synthetic flexibilities and design potential to the arsenal of anticancer therapeutics, which can be limited by cytotoxicity to noncancerous cells and induction of drug resistance via stress-induced mutagenesis. Despite continued research efforts on membrane-perforating peptides for antimicrobial applications, success in anticancer peptide therapeutics remains elusive given the muted distinction between cancerous and normal cell membranes and the challenge of peptide degradation and neutralization upon intravenous delivery. Using triple-negative breast cancer as a model, the authors report the development of a new class of anticancer peptides. Through function-conserving mutations, the authors achieved cancer cell selective membrane perforation, with leads exhibiting a 200-fold selectivity over non-cancerogenic cells and superior cytotoxicity over doxorubicin against breast cancer tumorspheres. Upon continuous exposure to the anticancer peptides at growth-arresting concentrations, cancer cells do not exhibit resistance phenotype, frequently observed under chemotherapeutic treatment. The authors further demonstrate efficient encapsulation of the anticancer peptides in 20 nm polymeric nanocarriers, which possess high tolerability and lead to effective tumor growth inhibition in a mouse model of MDA-MB-231 triple-negative breast cancer. This work demonstrates a multidisciplinary approach for enabling translationally relevant membrane-lytic peptides in oncology, opening up a vast chemical repertoire to the arms race against cancer.

Synthetic molecular evolution of antimicrobial peptides

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

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

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

Antimicrobial Peptide Mechanisms Studied by Whole-Cell Deuterium NMR

Much of the work probing antimicrobial peptide (AMP) mechanisms has focussed on how these molecules permeabilize lipid bilayers. However, AMPs must also traverse a variety of non-lipid cell envelope components before they reach the lipid bilayer. Additionally, there is a growing list of AMPs with non-lipid targets inside the cell. It is thus useful to extend the biophysical methods that have been traditionally applied to study AMP mechanisms in liposomes to the full bacteria, where the lipids are present along with the full complexity of the rest of the bacterium. This review focusses on what can be learned about AMP mechanisms from solid-state NMR of AMP-treated intact bacteria. It also touches on flow cytometry as a complementary method for measuring permeabilization of bacterial lipid membranes in whole bacteria.

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.

Molecular dynamics study of the internalization of cell-penetrating peptides containing unnatural amino acids across membranes

Peptide-based delivery systems that deliver target molecules into cells have been gaining traction. These systems need cell-penetrating peptides (CPPs), which have the remarkable ability to penetrate into biological membranes and help internalize different cargoes into cells through the cell membranes. The molecular internalization mechanism and structure-function relationships of CPPs are not clear, although the incorporation of nonproteinogenic amino acids such as α-aminoisobutyric acid (Aib) has been reported to increase their helicity, biostability and penetration efficiencies. Here, we used molecular dynamics to study two Aib-containing CPPs, poly(LysAibAla)₃ (KAibA) and poly(LysAibGly)₃ (KAibG), that previously showed high cell internalization efficiency. KAibA and KAibG displayed the lowest internalization energies among the studied CPPs, showing distinct internalization mechanisms depending on the lipid composition of the model membranes. The presence of Aib residues allows these CPPs to adopt amphipathic folding to efficiently penetrate through the membranes. Elucidating how Aib incorporation affects CPP-membrane binding and interactions is beneficial for the design of CPPs for efficient intracellular delivery.

Helical structure motifs made searchable for functional peptide design

The systematic design of functional peptides has technological and therapeutic applications. However, there is a need for pattern-based search engines that help locate desired functional motifs in primary sequences regardless of their evolutionary conservation. Existing databases such as The Protein Secondary Structure database (PSS) no longer serves the community, while the Dictionary of Protein Secondary Structure (DSSP) annotates the secondary structures when tertiary structures of proteins are provided. Here, we extract 1.7 million helices from the PDB and compile them into a database (Therapeutic Peptide Design database; TP-DB) that allows queries of compounded patterns to facilitate the identification of sequence motifs of helical structures. We show how TP-DB helps us identify a known purification-tag-specific antibody that can be repurposed into a diagnostic kit for Helicobacter pylori. We also show how the database can be used to design a new antimicrobial peptide that shows better Candida albicans clearance and lower hemolysis than its template homologs. Finally, we demonstrate how TP-DB can suggest point mutations in helical peptide blockers to prevent a targeted tumorigenic protein-protein interaction. TP-DB is made available at http://dyn.life.nthu.edu.tw/design/ .

Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes

Understanding the interactions between peptides and lipid membranes could not only accelerate the development of antimicrobial peptides as treatments for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm improvements, and more accurate parameterizations (i.e., force fields), all-atom molecular dynamics (MD) simulations have been used as a "computational microscope" to investigate the molecular interactions and mechanisms of membrane-active peptides in cell membranes (Chen et al., Curr Opin Struct Biol 61:160-166, 2020; Ulmschneider and Ulmschneider, Acc Chem Res 51(5):1106-1116, 2018; Dror et al., Annu Rev Biophys 41:429-452, 2012). In this chapter, we describe how to utilize MD simulations to predict and study peptide dynamics and how to validate the simulations by circular dichroism, intrinsic fluorescent probe, membrane leakage assay, electrical impedance, and isothermal titration calorimetry. Experimentally validated MD simulations open a new route towards peptide design starting from sequence and structure and leading to desirable functions.

Plant Antimicrobial Peptides as Potential Tool for Topic Treatment of Hidradenitis Suppurativa

Among chronic skin autoinflammatory diseases, Hidradenitis Suppurativa (HS) stands out for its chronicity, highly variable condition, and profound impact on the patients' quality of life. HS is characterized by suppurative skin lesions in diverse body areas, including deep-seated painful nodules, abscesses, draining sinus, and bridged scars, among others, with typical topography. To date, HS is considered a refractory disease and medical treatments aim to reduce the incidence, the infection, and the pain of the lesions. For this purpose, different classes of drugs, including anti-inflammatory molecules, antibiotics and biological drugs are being used. Antimicrobial peptides (AMPs), also called defense peptides, emerge as a new class of therapeutic compounds, with broad-spectrum antimicrobial action, in addition to reports on their anti-inflammatory, healing, and immunomodulating activity. Such peptides are present in prokaryotes and eukaryotes, as part of the innate eukaryotic immune system. It has been proposed that a deregulation in the expression of AMPs in human epithelial tissues of HS patients may be associated with the etiology of this skin disease. In this scenario, plant AMPs stand out for their richness, diversity of types, and broad antimicrobial effects, with potential application for topical systemic use in patients affected by HS.

Ab initio Designed Antimicrobial Peptides Against Gram-Negative Bacteria

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

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

Evaluation of Host Defense Peptide (CaD23)-Antibiotic Interaction and Mechanism of Action: Insights From Experimental and Molecular Dynamics Simulations Studies

Background/Aim: Host defense peptides (HDPs) have the potential to provide a novel solution to antimicrobial resistance (AMR) in view of their unique and broad-spectrum antimicrobial activities. We had recently developed a novel hybrid HDP based on LL-37 and human beta-defensin-2, named CaD23, which was shown to exhibit good in vivo antimicrobial efficacy against Staphylococcus aureus in a bacterial keratitis murine model. This study aimed to examine the potential CaD23-antibiotic synergism and the secondary structure and underlying mechanism of action of CaD23. Methods: Peptide-antibiotic interaction was evaluated against S. aureus, methicillin-resistant S. aureus (MRSA), and Pseudomonas aeruginosa using established checkerboard and time-kill assays. Fractional inhibitory concentration index (FICI) was calculated and interpreted as synergistic (FIC<0.5), additive (FIC between 0.5-1.0), indifferent (FIC between >1.0 and ≤4), or antagonistic (FIC>4). SYTOX green uptake assay was performed to determine the membrane-permeabilising action of CaD23. Molecular dynamics (MD) simulations were performed to evaluate the interaction of CaD23 with bacterial and mammalian mimetic membranes. Circular dichroism (CD) spectroscopy was also performed to examine the secondary structures of CaD23. Results: CaD23-amikacin and CaD23-levofloxacin combination treatment exhibited a strong additive effect against S. aureus SH1000 (FICI = 0.60-0.69) and MRSA43300 (FICI = 0.56-0.60) but an indifferent effect against P. aeruginosa (FIC = 1.03-1.15). CaD23 (at 25 μg/ml; 2xMIC) completely killed S. aureus within 30 min. When used at sub-MIC concentration (3.1 μg/ml; 0.25xMIC), it was able to expedite the antimicrobial action of amikacin against S. aureus by 50%. The rapid antimicrobial action of CaD23 was attributed to the underlying membrane-permeabilising mechanism of action, evidenced by the SYTOX green uptake assay and MD simulations studies. MD simulations revealed that cationicity, alpha-helicity, amphiphilicity and hydrophobicity (related to the Trp residue at C-terminal) play important roles in the antimicrobial action of CaD23. The secondary structures of CaD23 observed in MD simulations were validated by CD spectroscopy. Conclusion: CaD23 is a novel alpha-helical, membrane-active synthetic HDP that can enhance and expedite the antimicrobial action of antibiotics against Gram-positive bacteria when used in combination. MD simulations serves as a powerful tool in revealing the peptide secondary structure, dissecting the mechanism of action, and guiding the design and optimisation of HDPs.

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.

Rational design of hyperstable antibacterial peptides for food preservation

We describe the design of peptides with properties like thermostability, pH stability, and antibacterial activity against a few bacterial food pathogens. Insights obtained from classical structure-function analysis of natural peptides and their mutants through antimicrobial and enzymatic assays are used to rationally develop a set of peptides. pH and thermostability assays were performed to demonstrate robust antimicrobial activity post-treatment with high temperatures and at wide pH ranges. We have also investigated the mode of action of these hyperstable peptides using membrane permeability assays, electron microscopy, and molecular dynamics simulations. Notably, through mutational studies, we show that these peptides elicit their antibacterial action via both membrane destabilization and inhibition of intracellular trypsin-the two functions attributable to separate peptide segments. Finally, toxicity studies and food preservation assays demonstrate the safety and efficacy of the designed peptides for food preservation. Overall, the study provides a general 'blueprint' for the development of stable antimicrobial peptides (AMPs). Insights obtained from this work may also be combined with combinatorial methods in high-throughput studies for future development of antimicrobials for various applications.

Molecular Insights into Pore Formation Mechanism, Membrane Perturbation, and Water Permeation by the Antimicrobial Peptide Pleurocidin: A Combined All-Atom and Coarse-Grained Molecular Dynamics Simulation Study

The antimicrobial peptide (AMP) pleurocidin has a broad antimicrobial activity against Gram-negative and Gram-positive bacteria by perturbation and permeabilizing their membranes; however, understanding the mechanism of action of pleurocidin, a promising AMP for replacing current antibiotic agents, has tremendous importance for future applications. Hence, we applied all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations to provide molecular-level insights into the pore-forming process. The early stages of pore formation were examined by 500 ns AA simulations. The results demonstrated that pleurocidin has the ability to create a pore with two peptides through which water molecules can flow. However, the results of the 25 μs CG simulations indicate that the final pore will be created by accumulation of more than two peptides. The results show that after 2.5 μs of simulations, peptides will aggregate and create a channel-like pore across the membrane. Pleurocidin can construct a more efficient and stable pore in the anionic membranes than in the zwitterionic membranes. Moreover, the structure amphipathicity, polarity, and basic residues play crucial roles in the pore formation and flow of water molecules across the lipid bilayers. In general, the findings revealed that based on the lipid compositions of the membranes, pleurocidin could act by forming either toroidal or disordered toroidal pores with different peptide arrangements.

Nanocapsule designs for antimicrobial resistance

The pressing need of new antimicrobial products is growing stronger, particularly because of widespread antimicrobial resistance, endangering our ability to treat common infections. The recent coronavirus pandemic has dramatically highlighted the necessity of effective antibacterial and antiviral protection. This work explores at the molecular level the mechanism of action of antibacterial nanocapsules assembled in virus-like particles, their stability and their interaction with mammal and antimicrobial model membranes. We use Molecular Dynamics with force-fields of different granularity and protein design strategies to study the stability, self-assembly and membrane poration properties of these nanocapsules.

Synthetic Peptides Derived From Lycosa Erythrognatha Venom: Interaction With Phospholipid Membranes and Activity Against Resistant Bacteria

Superbugs are a public health problem, increasing the need of new drugs and strategies to combat them. Our group has previously identified LyeTxI, an antimicrobial peptide isolated from Lycosa erythrognatha spider venom. From LyeTxI, we synthesized and characterized a derived peptide named LyeTxI-b, which has shown significant in vitro and in vivo activity. In this work, we elucidate the interaction of LyeTxI-b with artificial membranes as well as its effects on resistant strains of bacteria in planktonic conditions or biofilms. Isothermal titration calorimetry revealed that LyeTxI-b interacts more rapidly and with higher intensity with artificial vesicles, showing higher affinity to anionic vesicles, when compared to synthetic LyeTxI. In calcein experiments, LyeTxI-b caused greater levels of vesicle cleavage. Both peptides showed antibacterial activity at concentrations of μmol L⁻¹ against 12 different clinically isolated strains, in planktonic conditions, in a concentration-dependent manner. Furthermore, both peptides elicited a dose-dependent production of reactive oxygen species in methicillin-resistant Staphylococcus aureus. In S. aureus biofilm assay, LyeTxI-b was more potent than LyeTxI. However, none of these peptides reduced Escherichia coli biofilms. Our results show LyeTxI-b as a promising drug against clinically resistant strains, being a template for developing new antibiotics.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.

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

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

Structural Disruptions of the Outer Membranes of Gram-Negative Bacteria by Rationally Designed Amphiphilic Antimicrobial Peptides

Gram-negative bacteria are covered by both an inner cytoplasmic membrane (IM) and an outer membrane (OM). Antimicrobial peptides (AMPs) must first permeate through the OM and cell wall before attacking the IM to cause cytoplasmic leakage and kill the bacteria. The bacterial OM is an asymmetric bilayer with the outer leaflet primarily composed of lipopolysaccharides (LPSs) and the inner leaflet composed of phospholipids (PLs). Two cationic α-helical AMPs were designed to target Gram-negative bacteria, a full peptide G(IIKK)₃I-NH₂ (G₃), and a hydrophobic lipopeptide C₈-G(IIKK)₂I-NH₂ (C₈G₂, with C₈ denoting the octanoyl chain). LPS dominates OM functions as the first line of defense against antibiotics, thereby reducing drug susceptibility. This work explores how the two AMPs interact with LPS through several carefully chosen OM models that facilitated measurements from solid-state nuclear magnetic resonance (ss-NMR), small-angle neutron scattering (SANS), and neutron reflectivity (NR). The results revealed that G₃ molecules bound preferably to the LPS head region and functioned as bridge molecules to reassemble the dislocated lipids into bilayer stacks. In contrast, C₈G₂ lipopeptides could quickly penetrate into the central region of the OM to cause direct removal of some membrane lipids. Different structural disruptions implicated different antimicrobial efficacies from these AMPs. The demonstration of the structural features underlying different susceptibilities of the OM to AMPs offers a useful route for the future development of strain-specific AMPs against antimicrobial-resistant pathogens.

Molecular Simulations Guidelines for Biological Nanomaterials: From Peptides to Membranes

In studying biological processes and focusing on the molecular mechanisms at the basis of these, molecular dynamics (MD) simulations have demonstrated to be a very useful tool for the past 50 years. This suite of computational methods calculates the time-dependent evolution of a molecular system using physics-based first principles. In this chapter, we give a brief introduction to the theory and practical use of molecular dynamics simulations, highlighting the different models and algorithms that have been developed to tackle specific problems, with a special focus on classical force fields. Some examples of how simulations have been used in the past will help the reader in discerning their power, limitations, and significance.

Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions

De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.

Harnessing molecular recognition for localized drug delivery

A grand challenge in drug delivery is providing the right dose, at the right anatomic location, for the right duration of time to maximize therapeutic efficacy while minimizing off-target toxicity and other deleterious side-effects. Two general modalities are receiving broad attention for localized drug delivery. In the first, referred to as "targeted accumulation", drugs or drug carriers are engineered to have targeting moieties that promote their accumulation at a specific tissue site from circulation. In the second, referred to as "local anchoring", drugs or drug carriers are inserted directly into the tissue site of interest where they persist for a specified duration of time. This review surveys recent advances in harnessing molecular recognition between proteins, peptides, nucleic acids, lipids, and carbohydrates to mediate targeted accumulation and local anchoring of drugs and drug carriers.

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

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

Membrane Binding of Antimicrobial Peptides Is Modulated by Lipid Charge Modification

Peptide interactions with lipid bilayers play a key role in a range of biological processes and depend on electrostatic interactions between charged amino acids and lipid headgroups. Antimicrobial peptides (AMPs) initiate the killing of bacteria by binding to and destabilizing their membranes. The multiple peptide resistance factor (MprF) provides a defense mechanism for bacteria against a broad range of AMPs. MprF reduces the negative charge of bacterial membranes through enzymatic conversion of the anionic lipid phosphatidyl glycerol (PG) to either zwitterionic alanyl-phosphatidyl glycerol (Ala-PG) or cationic lysyl-phosphatidyl glycerol (Lys-PG). The resulting change in the membrane charge is suggested to reduce the binding of AMPs to membranes, thus impeding downstream AMP activity. Using coarse-grained molecular dynamics to investigate the effects of these modified lipids on AMP binding to model membranes, we show that AMPs have substantially reduced affinity for model membranes containing Ala-PG or Lys-PG. More than 5000 simulations in total are used to define the relationship between lipid bilayer composition, peptide sequence (using five different membrane-active peptides), and peptide binding to membranes. The degree of interaction of a peptide with a membrane correlates with the membrane surface charge density. Free energy profile (potential of mean force) calculations reveal that the lipid modifications due to MprF alter the energy barrier to peptide helix penetration of the bilayer. These results will offer a guide to the design of novel peptides, which addresses the issue of resistance via MprF-mediated membrane modification.

Molecular Dynamics Simulation of the Interaction of Two Linear Battacin Analogs with Model Gram-Positive and Gram-Negative Bacterial Cell Membranes

Antimicrobial peptides (AMPs) are a potential solution to the increasing threat of antibiotic resistance, but successful design of active but nontoxic AMPs requires understanding their mechanism of action. Molecular dynamics (MD) simulations can provide atomic-level information regarding how AMPs interact with the cell membrane. Here, we have used MD simulations to study two linear analogs of battacin, a naturally occurring cyclic, lipidated, nonribosomal AMP. Like battacin, these analogs are active against Gram-negative multidrug resistant and Gram-positive bacteria, but they are less toxic than battacin. Our simulations show that this activity depends upon a combination of positively charged and hydrophobic moieties. Favorable interactions with negatively charged membrane lipid head groups drive association with the membrane and insertion of hydrophobic residues, and the N-terminal lipid anchors the peptides to the membrane surface. Both effects are required for stable membrane binding.

How do Self-Assembling Antimicrobial Lipopeptides Kill Bacteria?

Antimicrobial peptides are promising alternatives to traditional antibiotics. A group of self-assembling lipopeptides was formed by attaching an acyl chain to the N-terminus of α-helix-forming peptides with the sequence C_x-G(IIKK)_yI-NH₂ (C_xG_y, x = 4-12 and y = 2). C_xG_y self-assemble into nanofibers above their critical aggregation concentrations (CACs). With increasing x, the CACs decrease and the hydrophobic interactions increase, promoting secondary structure transitions within the nanofibers. Antimicrobial activity, determined by the minimum inhibition concentration (MIC), also decreases with increasing x, but the MICs are significantly smaller than the CACs, suggesting effective bacterial membrane-disrupting power. Unlike conventional antibiotics, both C₈G₂ and C₁₂G₂ can kill Staphylococcus aureus and Escherichia coli after only minutes of exposure under the concentrations studied. C₁₂G₂ nanofibers have considerably faster killing dynamics and lower cytotoxicity than their nonaggregated monomers. Antimicrobial activity of peptide aggregates has, to date, been underexploited, and it is found to be a very promising mechanism for peptide design. Detailed evidence for the molecular mechanisms involved is provided, based on superresolution fluorescence microscopy, solid-state nuclear magnetic resonance, atomic force microscopy, neutron scattering/reflectivity, circular dichroism, and Brewster angle microscopy.