The antimicrobial peptides magainin 2 and PGLa, discovered in the skin of the African clawed frog, Xenopus laevis, exhibit marked synergism [Westerhoff, H. V., Zasloff, M., Rosner, J. L., Hendler, R. W., de Waal, A., Vaz Gomes, A., Jongsma, A. P. M., Riethorst, A., and Juretic, D., Eur. J. Biochem. 228, 257-264 (1995)], although the mechanism is not yet clear. They are believed to kill bacteria by permeabilizing membranes. In this study, we examined the interactions of these peptides in lipid bilayers. PGLa, like magainin 2, preferentially interacts with acidic lipids, forming an amphipathic helix. The peptide induces the release of a water-soluble dye, calcein, entrapped within liposomes. The coexistence of magainin 2 enhances membrane permeabilization, which is maximal at a 1:1 molar ratio. Fluorescence experiments using L18W-PGLa revealed that both peptides form a stoichiometric 1:1 complex in the membrane phase with an association free energy of -15 kJ/mol. Single amino acid mutations in magainin 2 significantly altered the synergistic activity, suggesting that precise molecular recognition is involved in complex formation. The complex as well as each component peptide form peptide-lipid supramolecular complex pores, which mediate the mutually coupled transbilayer transport of dye, lipid, and the peptide per se. The rate of pore formation rate is in the order complex >/= PGLa > magainin 2, whereas the pore lifetime is in the order magainin 2 > complex > PGLa. Therefore, the synergism is a consequence of the formation of a potent heterosupramolecular complex, which is characterized by fast pore formation and moderate pore stability.

Mechanism of Synergism between Antimicrobial Peptides, Magainin 2 and PGLa

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

/pdf/dbaasp-v3-database-of-antimicrobial-cytotoxic-activity-and-3uf8gh1ru3.pdf

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics.

Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance of AMP optimization. In this context, the computer-aided design of AMPs has put together crucial information on chemical parameters and bioactivities in AMP sequences, thus providing modes of prediction to evaluate the antibacterial potential of a candidate sequence before synthesis. Quantitative structure-activity relationship (QSAR) computational models, for instance, have greatly contributed to AMP sequence optimization aimed at improved biological activities. In addition to machine-learning methods, the de novo design, linguistic model, pattern insertion methods, and genetic algorithms, have shown the potential to boost the automated design of AMPs. However, how successful have these approaches been in generating effective antibacterial drug candidates? Bearing this in mind, this review will focus on the main computational strategies that have generated AMPs with promising activities against pathogenic bacteria, as well as anti-infective potential in different animal models, including sepsis and cutaneous infections. Moreover, we will point out recent studies on the computer-aided design of antibiofilm peptides. As expected from automated design strategies, diverse candidate sequences with different structural arrangements have been generated and deposited in databases. We will, therefore, also discuss the structural diversity that has been engendered.

/pdf/computer-aided-design-of-antimicrobial-peptides-are-we-1zrczn1pmv.pdf

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

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. A computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.

/pdf/accelerated-antimicrobial-discovery-via-deep-generative-3clya0tsem.pdf

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Antimicrobial Peptides (AMPs) are compounds, which have inhibitory activity against microorganisms. In the last decades, AMPs have become powerful alternative agents that have met the need for novel anti-infectives to overcome increasing antibiotic resistance problems. Moreover, recent epidemics and pandemics are increasing the popularity of AMPs, due to the urgent necessity for effective antimicrobial agents in combating the new emergence of microbial diseases. AMPs inhibit a wide range of microorganisms through diverse and special mechanisms by targeting mainly cell membranes or specific intracellular components. In addition to extraction from natural sources, AMPs are produced in various hosts using recombinant methods. More recently, the synthetic analogs of AMPs, designed with some modifications, are predicted to overcome the limitations of stability, toxicity, and activity associated with natural AMPs. AMPs have potential applications as antimicrobial agents in food, agriculture, environment, animal husbandry, and pharmaceutical industries. In this review, we have provided an overview of the structure, classification, and mechanism of action of AMPs, as well as discussed opportunities for their current and potential applications.

Antimicrobial peptides (AMPs): A promising class of antimicrobial compounds.

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics to combat bacterial resistance to conventional drugs. The design of de novo AMPs with high therapeutic indexes, low cost of synthesis, high resistance to proteases and high bioavailability remains a challenge. Such design requires computational modeling of antimicrobial properties. Currently, most computational methods cannot accurately calculate antimicrobial potency against particular strains of bacterial pathogens. We developed a tool for AMP prediction (Special Prediction (SP) tool) and made it available on our Web site (https://dbaasp.org/prediction). Based on this tool, a simple algorithm for the design of de novo AMPs (DSP) was created. We used DSP to design short peptides with high therapeutic indexes against gram-negative bacteria. The predicted peptides have been synthesized and tested in vitro against a panel of gram-negative bacteria, including drug resistant ones. Predicted activity against Escherichia coli ATCC 25922 was experimentally confirmed for 14 out of 15 peptides. Further improvements for designed peptides included the synthesis of D-enantiomers, which are traditionally used to increase resistance against proteases. One synthetic D-peptide (SP15D) possesses one of the lowest values of minimum inhibitory concentration (MIC) among all DBAASP database short peptides at the time of the submission of this article, while being highly stable against proteases and having a high therapeutic index. The mode of anti-bacterial action, assessed by fluorescence microscopy, shows that SP15D acts similarly to cell penetrating peptides. SP15D can be considered a promising candidate for the development of peptide antibiotics. We plan further exploratory studies with the SP tool, aiming at finding peptides which are active against other pathogenic organisms.

De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria.

Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.

https://www.mdpi.com/1424-8247/14/5/471/pdf

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

Development of the new antimicrobial agents against antibiotic resistance pathogens is the nowadays challenge. Antimicrobial peptides (AMP) occur as important defence agents in many organisms and offer a viable alternative to conventional antibiotics. Therefore they have become increasingly recognized in current research as templates for prospective antibiotic agents. The efficient designing of the new antimicrobials on the basis of antimicrobial peptides requires comprehensive knowledge on those general physical-chemical characteristics which allow to differ antimicrobial peptides from non-active against microbs ones. According to supposed mechanisms of action, AMP interact with and physically disrupt the bacterial membranes. Consequently, hydrophobicity, amphiphilicity and intrinsic aggregation propensities are considered as such major characteristics of the peptide, which determine the results of peptide-membrane interactions. For some kind of peptides such characteristics as hydrophobicity, amphiphilicity and aggregation bias determines their ability to compose transmembrane domain of the membrane protein, whilst for others the same properties are respond for their antimicrpobial activity, i.e. give them ability of membrane permeability and its damage. In this review we analyze the data about hydrophobicity, amphiphilicity and intrinsic aggregation propensities available in literature in order to compare antimicrobial and transmembrane peptides and show what is the common and what is the difference in this respect between them.

Transmembrane and Antimicrobial Peptides. Hydrophobicity, Amphiphilicity and Propensity to Aggregation

Antimicrobial peptides (AMPs) are anti-infectives that have potential as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides imply interaction with cell envelope. Comprehensive understanding of peculiarities of interactions of antimicrobial peptides with cell envelope is necessary to perform the task-oriented design of new biotherapeutics, against which for microbes it is hard to work out resistance. In order to enable a de novo design with low costs and in high throughput, in silico predictive models have to be required. To develop the performant predictive model, comprehensive knowledge on mechanisms of action of AMPs has to be possessed. The last knowledge will allow us to encode amino acid sequences expressively and to get success to the choosing of the accurate classifier of AMPs. A shared protective layer of microbial cells is inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) is the most comprehensively studied. We provide a review of mechanisms and results of interaction of AMP with the cell membrane, relying on the survey of physicochemical, aggregative and structural features of AMPs. Potency and mechanism of action of AMP have presented in the terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is in such physicochemical features of peptides as the hydrophobicity, hydrophilicity, and amphiphilicity. Many different approaches were used to classify AMPs. The survey of the knowledge on sequences, structures, and modes of actions of AMP, allows concluding that, only the physicochemical features of AMPs give the capability to perform the unambiguous classification. Comprehensive knowledge of physicochemical features of AMP is necessary to develop task-oriented methods of design of peptide-based antibiotics de novo.

Physicochemical Features and Peculiarities of Interaction of Antimicrobial Peptides with the Membrane

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics. There are a lot of computational methods of AMP prediction. Although most of them can predict antimicrobial potency against any microbe (microbe is not identified) with rather high accuracy, prediction quality of these tools against particular bacterial strains is low [1,2]. Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org [3]). In this presentation we describe this tool and predictive models for some Gram positive bacterial strains (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) and a model for the prediction of hemolytic activity. Predictive model for Gram negative Escherichia coli ATCC 25922 was presented earlier [2,4]. Special prediction tool can be used for the design of peptides being active against particular strain. To demonstrate the capability of the tool, peptides predicted as active against E-coli ATCC 25922 and Staphylococcus aureus ATCC 25923 have been synthesized, and tested in vitro. The results have shown the justification of using special prediction tool for the design of new AMPs

/pdf/dbaasp-special-prediction-as-a-tool-for-the-prediction-of-2qz5acsap4.pdf

Maya Grigolava

Papers

De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria.

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

Transmembrane and Antimicrobial Peptides. Hydrophobicity, Amphiphilicity and Propensity to Aggregation

Physicochemical Features and Peculiarities of Interaction of Antimicrobial Peptides with the Membrane

DBAASP Special prediction as a tool for the prediction of antimicrobial potency against particular target species