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

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

Unraveling the molecular dynamics of sugammadex-rocuronium complexation: A blueprint for cyclodextrin drug design.

Sugammadex, marketed as Bridion™, is an approved cyclodextrin (CD) based drug for the reversal of neuromuscular blockade in adults undergoing surgery. Sugammadex forms an inclusion complex with the neuromuscular blocking agent (NMBA) rocuronium, allowing rapid reversal of muscle paralysis. In silico methods have been developed for studying CD inclusion complexes, aimed at accurately predicting their structural, energetic, dynamic, and kinetic properties, as well as binding constants. Here, a computational study aimed at characterizing the sugammadex-rocuronium system from the perspective of docking calculations, free molecular dynamics (MD) simulations, and biased metadynamics simulations with potential of mean force (PMF) calculations is presented. The aim is to provide detailed information about this system, as well as to use it as a model system for validation of the methods. This method predicts results in line with experimental evidence for both the optimal structure and the quantitative value for the binding constant. Interestingly, there is a less profound preference for the orientation than might be assumed based on electrostatic interactions, suggesting that both orientations may exist in solution. These results show that this technology can efficiently analyze CD inclusion complexes and could be used to facilitate the development and optimization of novel applications for CDs.

Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico.

Cyclodextrins (CDs) are cyclic carbohydrate polymers that hold significant promise for drug delivery and industrial applications. Their effectiveness depends on their ability to encapsulate target molecules with strong affinity and specificity, but quantifying affinities in these systems accurately is challenging for a variety of reasons. Computational methods represent an exceptional complement to in vitro assays because they can be employed for existing and hypothetical molecules, providing high resolution structures in addition to a mechanistic, dynamic, kinetic, and thermodynamic characterization. Here, we employ potential of mean force (PMF) calculations obtained from guided metadynamics simulations to characterize the 1:1 inclusion complexes between four different modified ßCDs, with different type, number, and location of substitutions, and two sterol molecules (cholesterol and 7-ketocholesterol). Our methods, validated for reproducibility through four independent repeated simulations per system and different post processing techniques, offer new insights into the formation and stability of CD-sterol inclusion complexes. A systematic distinct orientation preference where the sterol tail projects from the CD's larger face and significant impacts of CD substitutions on binding are observed. Notably, sampling only the CD cavity's wide face during simulations yielded comparable binding energies to full-cavity sampling, but in less time and with reduced statistical uncertainty, suggesting a more efficient approach. Bridging computational methods with complex molecular interactions, our research enables predictive CD designs for diverse applications. Moreover, the high reproducibility, sensitivity, and cost-effectiveness of the studied methods pave the way for extensive studies of massive CD-ligand combinations, enabling AI algorithm training and automated molecular design.

Overlay databank unlocks data-driven analyses of biomolecules for all.

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches.

The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.

Molecular insights into the effects of focused ultrasound mechanotherapy on lipid bilayers: Unlocking the keys to design effective treatments.

Administration of focused ultrasounds (US) represents an attractive complement to classical therapies for a wide range of maladies, from cancer to neurological pathologies, as they are non-invasive, easily targeted, their dosage is easy to control, and they involve low risks. Different mechanisms have been proposed for their activity but the direct effect of their interaction with cell membranes is not well understood at the molecular level. This is in part due to the difficulty of designing experiments able to probe the required spatio-temporal resolutions. Here we use Molecular Dynamics (MD) simulations at two resolution levels and machine learning (ML) classification tools to shed light on the effects that focused US mechanotherapy methods have over a range of lipid bilayers. Our results indicate that the dynamic-structural response of the membrane models to the mechanical perturbations caused by the sound waves strongly depends on the lipid composition. The analyses performed on the MD trajectories contribute to a better understanding of the behavior of lipid membranes, and to open up a path for the rational design of new therapies for the long list of diseases characterized by specific lipid profiles of pathological membrane cells.

The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies.

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

Uncovering the mechanisms of cyclic peptide self-assembly in membranes with the chirality-aware MA(R/S)TINI forcefield.

Cyclic peptides (CPs) formed by alternation of D- and L-amino acids (D,L-CPs) can self-assemble into nanotubes (SCPNs) by parallel or/and antiparallel stacking. Different applications have been attributed to these nanotubes, including the disruption of lipid bilayers of specific compositions and the selective transport of ions throughout membranes. Molecular dynamics (MD) simulations have significantly contributed to understand the interaction between CPs, including the structural, dynamic and transport properties of their supramolecular aggregates. The high computational cost of atomic resolution forcefields makes them impractical for simulating the self-assembly of macromolecules, so coarse-grained (CG) models might represent a more feasible solution for this purpose. However, general CG models used for the simulation of biomolecules such as the MARTINI forcefield do not explicitly consider the non-covalent interactions leading to the formation of secondary structure patterns in proteins. This becomes particularly important in the case of CPs due to the D- and L-chirality alternation in their sequence, leading to opposite orientations of the backbone polar groups on both sides of the cyclic ring plane. In order to overcome this limitation, we have extended the MARTINI forcefield to introduce chirality in each residue of the CPs. The new parametrization, which we have called MA(R/S)TINI, reproduces the expected self-assembly patterns for several CP sequences in the presence of different membrane models, explicitly considering the chirality of the CPs and with no significant extra computational cost. Our simulations provide new mechanistic information of how these systems self-assemble in presence of different lipid scenarios, showing that the CP-CP and CP-membrane interactions are sensitive to the peptide sequence chirality. This opens the door to design new bioactive CPs based on CG-MD simulations. A web-based tool for the automatic parameterization of new CP sequences using MA(R/S)TINI, among other functionalities, is under construction (see http://cyclopep.com).

Unravelling hierarchical levels of structure in lipid membranes.

In analogy with the hierarchical levels typically used to describe the structure of nucleic acids or proteins and keeping in mind that lipid bilayers are not just mere envelopers for biological material but directly responsible for many important functions of life, it is discussed here how membrane models can also be interpreted in terms of different hierarchies in their structure. Namely, lipid composition, interaction between leaflets, existence and interaction of domains arising from the coordinate behavior of lipids and their properties, plus the manifest and specific perturbation of the lipid organization around macromolecules embedded in a membrane are hereby used to define the primary, secondary, tertiary and quaternary structures, respectively. Molecular Dynamics simulations are used to illustrate this proposal. Alternative levels of organization and methods to define domains can be proposed but the final aim is to highlight the paradigm arising from this description which is expected to have significant consequences on deciphering the underlying factors governing membranes and their interactions with other molecules.

Not so rigid capsids based on cyclodextrin complexes: Keys to design.

HYPOTHESIS: Membranes based on cyclodextrin complexes can be used as functional nanocarrier envelopers by chemical modifications of the cyclodextrin hydroxyl groups or by encapsulating different ligands in their cavities. EXPERIMENTS: Molecular dynamics simulations of monolayers and bilayers based on supramolecular complexes consisting of two α or ß-cyclodextrin and one sodium dodecylsulfate or dodecane at 283 K and at 298 K were performed. FINDINGS: It is shown that the structure and main interactions stabilizing the membranes, as well as their permeability to water and ions can be tuned by changing the cyclodextrin, the ligand, the number of layers or/and the temperature. These results provide new evidences about both their dynamic nature and the interactions responsible for the stabilization of the membranes and will facilitate the design of new functional capsides and applications based on cyclodextrin complexes.

Molecular Dynamics Simulations of Transmembrane Cyclic Peptide Nanotubes Using Classical Force Fields, Hydrogen Mass Repartitioning, and Hydrogen Isotope Exchange Methods: A Critical Comparison.

Self-assembled cyclic peptide nanotubes with alternating D- and L-amino acid residues in the sequence of each subunit have attracted a great deal of attention due to their potential for new nanotechnology and biomedical applications, mainly in the field of antimicrobial peptides. Molecular dynamics simulations can be used to characterize these systems with atomic resolution at different time scales, providing information that is difficult to obtain via wet lab experiments. However, the performance of classical force fields typically employed in the simulation of biomolecules has not yet been extensively tested with this kind of highly constrained peptide. Four different classical force fields (AMBER, CHARMM, OPLS, and GROMOS), using a nanotube formed by eight D,L-α-cyclic peptides inserted into a lipid bilayer as a model system, were employed here to fill this gap. Significant differences in the pseudo-cylindrical cavities formed by the nanotubes were observed, the most important being the diameter of the nanopores, the number and location of confined water molecules, and the density distribution of the solvent molecules. Furthermore, several modifications were performed on GROMOS54a7, aiming to explore acceleration strategies of the MD simulations. The hydrogen mass repartitioning (HMR) and hydrogen isotope exchange (HIE) methods were tested to slow down the fastest degrees of freedom. These approaches allowed a significant increase in the time step employed in the equation of the motion integration algorithm, from 2 fs up to 5-7 fs, with no serious changes in the structural and dynamical properties of the nanopores. Subtle differences with respect to the simulations with the unmodified force fields were observed in the concerted movements of the cyclic peptides, as well as in the lifetime of several H-bonds. All together, these results are expected to contribute to better understanding of the behavior of self-assembled cyclic peptide nanotubes, as well as to support the methods tested to speed up general MD simulations; additionally, they do provide a number of quantitative descriptors that are expected to be used as a reference to design new experiments intended to validate and complement computational studies of antimicrobial cyclic peptides.

SuPepMem: A database of innate immune system peptides and their cell membrane interactions.

Host defense peptides (HDPs) are short cationic peptides that play a key role in the innate immune response of all living organisms. Their action mechanism does not depend on the presence of protein receptors, but on their ability to target and disrupt the membranes of a wide range of pathogenic and pathologic cells which are recognized by their specific compositions, typically with a relatively high concentration of anionic lipids. Lipid profile singularities have been found in cancer, inflammation, bacteria, viral infections, and even in senescent cells, enabling the possibility to use them as therapeutic targets and/or diagnostic biomarkers. Molecular dynamics (MD) simulations are extraordinarily well suited to explore how HDPs interact with membrane models, providing a large amount of qualitative and quantitative information that, nowadays, cannot be assessed by wet-lab methods at the same level of temporal and spatial resolution. Here, we present SuPepMem, an open-access repository containing MD simulations of different natural and artificial peptides with potential membrane lysis activity, interacting with membrane models of healthy mammal, bacteria, viruses, cancer or senescent cells. In addition to a description of the HDPs and the target systems, SuPepMem provides both the input files necessary to run the simulations and also the results of some selected analyses, including structural and MD-based quantitative descriptors. These descriptors are expected to be useful to train machine learning algorithms that could contribute to design new therapeutic peptides. Tools for comparative analysis between different HDPs and model membranes, as well as to restrict the queries to structural and time-averaged properties are also available. SuPepMem is a living project, that will continuously grow with more simulations including peptides of different sequences, MD simulations with different number of peptide units, more membrane models and also several resolution levels. The database is open to MD simulations from other users (after quality check by the SuPepMem team). SuPepMem is freely available under https://supepmem.com/.

Unsupervised bubble calorimetry analysis: Surface tension from isothermal titration calorimetry.

HYPOTHESIS: The injection of air into the sample cell of an isothermal titration calorimeter containing a liquid provides a rich-in-information signal, with a periodic contribution arising from the creation, growing and release of bubbles. The identification and analysis of such contributions allow the accurate determination of the surface tension of the target liquid. EXPERIMENTS: Air is introduced at a constant rate into the sample cell of the calorimeter containing either a pure liquid or a solution. The resulting calorimetric signal is analyzed by a new algorithm, which is implemented into a computational code. FINDINGS: The thermal power generated by our experiments is often noisy, thus hiding the periodic signal arising from the bubbles' formation and release. The new algorithm was tested with a range of different types of calorimetric raw data, some of them apparently being just noise. In all cases, the contribution of the bubbles to the signal was isolated and the corresponding period was successfully determined in an automated way. It is also shown that two reference measurements suffice to calibrate the instrument at a given temperature, regardless the injection rate, allowing the direct determination of surface tension values for the liquid contained in the sample cell.

Inverse Conformational Selection in Lipid-Protein Binding.

Interest in lipid interactions with proteins and other biomolecules is emerging not only in fundamental biochemistry but also in the field of nanobiotechnology where lipids are commonly used, for example, in carriers of mRNA vaccines. The outward-facing components of cellular membranes and lipid nanoparticles, the lipid headgroups, regulate membrane interactions with approaching substances, such as proteins, drugs, RNA, or viruses. Because lipid headgroup conformational ensembles have not been experimentally determined in physiologically relevant conditions, an essential question about their interactions with other biomolecules remains unanswered: Do headgroups exchange between a few rigid structures, or fluctuate freely across a practically continuous spectrum of conformations? Here, we combine solid-state NMR experiments and molecular dynamics simulations from the NMRlipids Project to resolve the conformational ensembles of headgroups of four key lipid types in various biologically relevant conditions. We find that lipid headgroups sample a wide range of overlapping conformations in both neutral and charged cellular membranes, and that differences in the headgroup chemistry manifest only in probability distributions of conformations. Furthermore, the analysis of 894 protein-bound lipid structures from the Protein Data Bank suggests that lipids can bind to proteins in a wide range of conformations, which are not limited by the headgroup chemistry. We propose that lipids can select a suitable headgroup conformation from the wide range available to them to fit the various binding sites in proteins. The proposed inverse conformational selection model will extend also to lipid binding to targets other than proteins, such as drugs, RNA, and viruses.

Simple ApproximaTion for Aggregation Number Determination by Isothermal Titration Calorimetry: STAND-ITC.

A new proposal to obtain aggregation numbers from isothermal titration calorimetry dilution experiments is described and tested using dodecyl trimethyl ammonium bromide, dodecyl methylimidazolium chloride, dodecyl methylimidazolium sulfonate, and didecyl methylimidazolium chloride aqueous solutions at different temperatures. The results were compared to those obtained from fluorescence measurements and also with data from the literature. In addition to the aggregation number, the molar free energy to transfer a solute molecule from the aggregate to the bulk solution, the enthalpy corresponding to the formation of the self-assembled suprastructures, the molar heat corresponding to the dilution of monomers and aggregates, and an offset parameter to account for unpredictable external contributions are simultaneously obtained using the same method. The new equations are compared to those obtained from previous proposals, and they are also analyzed in detail to assess the impact of each fitting parameter in the profile of the calorimetric isotherm. This new approach has been implemented in a computational code that automatically determines the fitting parameters as well as the corresponding statistical uncertainties for the large variety of calorimetric profiles that have been tested. Given the high sensitivity of the dilution experiments to the aggregation number for relatively small assemblies, our approach is proposed also to quantify the oligomerization state of biomolecules such as proteins and peptides.

Aggregation versus inclusion complexes to solubilize drugs with cyclodextrins. A case study using sulphobutylether-ß-cyclodextrins and remdesivir.

The formation of small hybrid aggregates between excipient and drug molecules is one of the mechanisms that contributes to the solubilization of active principles in pharmaceutical formulations. The characterization of the formation, governing interactions and structure of such entities is not trivial since they are highly flexible and dynamic, quickly exchanging molecules from one to another. In the case of cyclodextrins, this mechanism and the formation of inclusion complexes synergistically cooperate to favour the bioavailability of drugs. In a previous study we reported a detailed characterization of the possible formation of inclusion complexes with 1:1 stoichiometry between remdesivir, the only antiviral medication currently approved by the United States Food and Drug Administration for treating COVID-19, and sulphobutylether-ß-cyclodextrins. Here we extend our study to assess the role of the spontaneous aggregation in the solubilization of the same drug, by molecular dynamics simulations at different relative concentrations of both compounds. The number of sulphobutylether substitutions in the cyclodextrin structure and two different protonation states of the remdesivir molecule are considered. We aim to shed light in the solubilization mechanism of sulphobutylether-ß-cyclodextrins, broadly used as an excipient in many pharmaceutical formulations, in particular in the case of remdesivir as an active compound.

Transmembrane Self-Assembled Cyclic Peptide Nanotubes Based on α-Residues and Cyclic δ-Amino Acids: A Computational Study.

Self-assembling cyclic peptide nanotubes have been shown to function as synthetic, integral transmembrane channels. The combination of natural and nonnatural aminoacids in the sequence of cyclic peptides enables the control not only of their outer surface but also of the inner cavity behavior and properties, affecting, for instance, their permeability to different molecules including water and ions. Here, a thorough computational study on a new class of self-assembling peptide motifs, in which δ-aminocycloalkanecarboxylic acids are alternated with natural α-amino acids, is presented. The presence of synthetic δ-residues creates hydrophobic regions in these α,δ-SCPNs, which makes them especially attractive for their potential implementation in the design of new drug or diagnostic agent carrier systems. Using molecular dynamics simulations, the behavior of water molecules, different ions (Li+, Na+, K+, Cs+, and Ca2+), and their correspondent counter Cl- anions is extensively investigated in the nanoconfined environment. The structure and dynamics are mutually combined in a diving immersion inside these transmembrane channels to discover a fascinating submarine nanoworld where star-shaped water channels guide the passage of cations and anions therethrough.

Cyclodextrin dimers: A versatile approach to optimizing encapsulation and their application to therapeutic extraction of toxic oxysterols.

We have developed a novel class of specifically engineered, dimerized cyclodextrin (CD) nanostructures for the encapsulation of toxic biomolecules such as 7-ketocholesterol (7KC). 7KC accumulates over time and causes dysfunction in many cell types, linking it to several age-related diseases including atherosclerosis and age-related macular degeneration (AMD). Presently, treatments for these diseases are invasive, expensive, and show limited benefits. CDs are cyclic glucose oligomers utilized to capture small, hydrophobic molecules. Here, a combination of in silico, in vitro, and ex vivo methods is used to implement a synergistic rational drug design strategy for developing CDs to remove atherogenic 7KC from cells and tissues. Mechanisms by which CDs encapsulate sterols are discussed, and we conclude that covalently linked head-to-head dimers of ßCDs have substantially improved affinity for 7KC compared to monomers. We find that inclusion complexes can be stabilized or destabilized in ways that allow the design of CD dimers with increased 7KC selectivity while maintaining an excellent safety profile. These CD dimers are being developed as therapeutics to treat atherosclerosis and other debilitating diseases of aging.

Fluid interface calorimetry.

HYPOTHESIS: Amphiphilic molecules spontaneously adsorb to fluid polar-nonpolar interfaces. The timescale of such adsorption depends on the molecular size and structure of the solute. This process should be accompanied by a power heat exchange that could be detected by commercial isothermal calorimeters. EXPERIMENTS: Air is injected in the bulk of different aqueous solutions contained in the sample cell of an isothermal titration calorimeter. The formation of the resulting bubbles leads to a liquid/air interface to which the solute molecules spontaneously adsorb. Continuous injection experiments to produce multiple bubbles as well as experiments with static bubbles stand from the capillary tip, aiming to observe slow adsorption processes, were performed. FINDINGS: The power associated with the formation, growth and release of air bubbles in different liquids was measured. Different independent contributions that can be associated to the pressure change in the gas phase, the evaporation-condensation of the solvent, the increase of interfacial area, the change in the heat capacity of the sample cell content, and the release of the bubble were observed. The periodic pattern produced by the continuous injection of air at a constant rate is used to determine the surface tension of different liquids, including solutions of different molecules and (bio)macromolecules.

An immersive journey to the molecular structure of SARS-CoV-2: Virtual reality in COVID-19.

The era of the explosion of immersive technologies has bumped head-on with the coronavirus disease 2019 (COVID-19) global pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The proper understanding of the three-dimensional structures that compose the virus, as well as of those involved in the infection process and in treatments, is expected to contribute to the advance of fundamental and applied research against this pandemic, including basic molecular biology studies and drug design. Virtual reality (VR) is a powerful technology to visualize the biomolecular structures that are currently being identified for SARS-CoV-2 infection, opening possibilities to significant advances in the understanding of the disease-associate mechanisms and thus to boost new therapies and treatments. The present availability of VR for a large variety of practical applications together with the increasingly easiness, quality and economic access of this technology is transforming the way we interact with digital information. Here, we review the software implementations currently available for VR visualization of SARS-CoV-2 molecular structures, covering a range of virtual environments: CAVEs, desktop software, and cell phone applications, all of them combined with head-mounted devices like cardboards, Oculus Rift or the HTC Vive. We aim to impulse and facilitate the use of these emerging technologies in research against COVID-19 trying to increase the knowledge and thus minimizing risks before placing huge amounts of money for the development of potential treatments.