Proton diffusion on the surface of mixed lipid membranes highlights the role of membrane composition.

Proton circuits within biological membranes, the foundation of natural bioenergetic systems, are significantly influenced by the lipid compositions of different biological membranes. In this study, we investigate the influence of mixed lipid membrane composition on the proton transfer (PT) properties on the surface of the membrane. We track the excited-state PT (ESPT) process from a tethered probe to the membrane with timescales and length scales of PT relevant to bioenergetic systems. Two processes can happen during ESPT: the initial PT from the probe to the membrane at short timescales, followed by diffusion of dissociated protons around the probe on the membrane, and the possible geminate recombination with the probe at longer timescales. Here, we use membranes composed of mixtures of phosphatidylcholine (PC) and phosphatidic acid (PA). We show that the changes in the ESPT properties are not monotonous with the concentration of the lipid mixture; at a low concentration of PA in PC, we find that the membrane is a poor proton acceptor. Molecular dynamics simulations indicate that the membrane is more structured at this specific lipid mixture, with the least number of defects. Accordingly, we suggest that the structure of the membrane is an important factor in facilitating PT. We further show that the composition of the membrane affects the geminate proton diffusion around the probe, whereas, on a timescale of tens of nanoseconds, the dissociated proton is mostly lateral restricted to the membrane plane in PA membranes, while in PC, the diffusion is less restricted by the membrane.

Multifaceted Activity of Fabimycin: Insights from Molecular Dynamics Studies on Bacterial Membrane Models.

Membranes─cells' essential scaffolds─are valid molecular targets for substances with an antimicrobial effect. While certain substances, such as octenidine, have been developed to target membranes for antimicrobial purposes, the recently reported molecule, fabimycin (F2B)─a novel agent targeting drug-resistant Gram-negative bacteria─has not received adequate attention regarding its activity on membranes in the literature. The following study aims to investigate the effects of F2B on different bacterial membrane models, including simple planar bilayers and more complex bilayer systems that mimic the Escherichia coli shell equipped with double inner and outer bilayers. Our results show that F2B exhibited more pronounced interactions with bacterial membrane systems compared to the control PC system. Furthermore, we observed significant changes in local membrane property homeostasis in both the inner and outer membrane models, specifically in the case of lateral diffusion, membrane thickness, and membrane resilience (compressibility, tilt). Finally, our results showed that the effect of F2B differed in a complex system and a single membrane system. Our study provides new insights into the multifaceted activity of F2B, demonstrating its potential to disrupt bacterial membrane homeostasis, indicating that its activity extends the currently known mechanism of FabI enzyme inhibition. This disruption, coupled with the ability of F2B to penetrate the outer membrane layers, sheds new light on the behavior of this antimicrobial molecule. This highlights the importance of the interaction with the membrane, crucial in combating bacterial infections, particularly those caused by drug-resistant strains.

Molecular guidelines for promising antimicrobial agents.

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

Diptool-A Novel Numerical Tool for Membrane Interactions Analysis, Applying to Antimicrobial Detergents and Drug Delivery Aids.

The widespread problem of resistance development in bacteria has become a critical issue for modern medicine. To limit that phenomenon, many compounds have been extensively studied. Among them were derivatives of available drugs, but also alternative novel detergents such as Gemini surfactants. Over the last decade, they have been massively synthesized and studied to obtain the most effective antimicrobial agents, as well as the most selective aids for nanoparticles drug delivery. Various protocols and distinct bacterial strains used in Minimal Inhibitory Concentration experimental studies prevented performance benchmarking of different surfactant classes over these last years. Motivated by this limitation, we designed a theoretical methodology implemented in custom fast screening software to assess the surfactant activity on model lipid membranes. Experimentally based QSAR (quantitative structure-activity relationship) prediction delivered a set of parameters underlying the Diptool software engine for high-throughput agent-membrane interactions analysis. We validated our software by comparing score energy profiles with Gibbs free energy from the Adaptive Biasing Force approach on octenidine and chlorhexidine, popular antimicrobials. Results from Diptool can reflect the molecule behavior in the lipid membrane and correctly predict free energy of translocation much faster than classic molecular dynamics. This opens a new venue for searching novel classes of detergents with sharp biologic activity.

A Systematic Approach: Molecular Dynamics Study and Parametrisation of Gemini Type Cationic Surfactants.

The spreading of antibiotic-resistant bacteria strains is one of the most serious problem in medicine to struggle nowadays. This triggered the development of alternative antimicrobial agents in recent years. One of such group is Gemini surfactants which are massively synthesised in various structural configurations to obtain the most effective antibacterial properties. Unfortunately, the comparison of antimicrobial effectiveness among different types of Gemini agents is unfeasible since various protocols for the determination of Minimum Inhibitory Concentration are used. In this work, we proposed alternative, computational, approach for such comparison. We designed a comprehensive database of 250 Gemini surfactants. Description of structure parameters, for instance spacer type and length, are included in the database. We parametrised modelled molecules to obtain force fields for the entire Gemini database. This was used to conduct in silico studies using the molecular dynamics to investigate the incorporation of these agents into model E. coli inner membrane system. We evaluated the effect of Gemini surfactants on structural, stress and mechanical parameters of the membrane after the agent incorporation. This enabled us to select four most likely membrane properties that could correspond to Gemini's antimicrobial effect. Based on our results we selected several types of Gemini spacers which could demonstrate a particularly strong effect on the bacterial membranes.

Unraveling the mechanism of octenidine and chlorhexidine on membranes: Does electrostatics matter?

The increasing problem of antibiotic resistance in bacteria requires the development of new antimicrobial candidates. There are several well-known substances with commercial use, but their molecular mode of action is not fully understood. In this work, we focus on two commonly used antimicrobial agents from the detergent family-octenidine dichloride (OCT) and chlorhexidine digluconate (CHX). Both of them are reported to be agents selectively attacking the cell membrane through interaction inducing membrane disruption by emulsification. They are believed to present electrostatic selectivity toward charged lipids. In this study, we tested this hypothesis and revised previously proposed molecular mechanisms of action. Employing a variety of techniques such as molecular dynamics, ζ potential with dynamic light scattering, vesicle fluctuation spectroscopy, carboxyfluorescein leakage measurement, and fluorescence trimethylammonium-diphenylhexatriene- and diphenylhexatriene-based studies for determination of OCT and CHX membrane location, we performed experimental studies using two model membrane systems-zwitterionic PC and negatively charged PG (18:1/18:1):PC (16:0/18:1) 3:7, respectively. These studies were extended by molecular dynamics simulations performed on a three-component bacterial membrane model system to further test interactions with another negatively charged lipid, cardiolipin. In summary, our study demonstrated that detergent selectivity is far more complicated than supposed simple electrostatic interactions. Although OCT does disrupt the membrane, our results suggest that its primary selectivity was more linked to mechanical properties of the membrane. On the other hand, CHX did not disrupt membranes as a primary activity, nor did it show any sign of electrostatic selectivity toward negatively charged membranes at any stage of interactions, which suggests membrane disruption by influencing more discrete membrane properties.