Faster but Not Sweeter: A Model of Escherichia coli Re-level Lipopolysaccharide for Martini 3 and a Martini 2 Version with Accelerated Kinetics.

Lipopolysaccharide (LPS) is a complex glycolipid molecule that is the main lipidic component of the outer leaflet of the outer membrane of Gram-negative bacteria. It has very limited lateral motion compared to phospholipids, which are more ubiquitous in biological membranes, including in the inner leaflet of the outer membrane of Gram-negative bacteria. The slow-moving nature of LPS can present a hurdle for molecular dynamics simulations, given that the (pragmatically) accessible timescales to simulations are currently limited to microseconds, during which LPS displays some conformational dynamics but hardly any lateral diffusion. Thus, it is not feasible to observe phenomena such as insertion of molecules, including antibiotics/antimicrobials, directly into the outer membrane from the extracellular side nor to observe LPS dissociating from proteins via molecular dynamics using currently available models at the atomistic and more coarse-grained levels of granularity. Here, we present a model of deep rough LPS compatible with the Martini 2 coarse-grained force field with scaled down nonbonded interactions to enable faster diffusion. We show that the faster-diffusing LPS model is able to reproduce the salient biophysical properties of the standard models, but due to its faster lateral motion, molecules are able to penetrate deeper into membranes containing the faster model. We show that the fast ReLPS model is able to reproduce experimentally determined patterns of interaction with outer membrane proteins while also allowing for LPS to associate and dissociate with proteins within microsecond timescales. We also complete the Martini 3 LPS toolkit for Escherichia coli by presenting a (standard) model of deep rough LPS for this force field.

Simulating Bacterial Membrane Models at the Atomistic Level: A Force Field Comparison.

Molecular dynamics (MD) simulations are currently an indispensable tool to understand both the dynamic and nanoscale organization of cell membrane models. A large number of quantitative parameters can be extracted from these simulations, but their reliability is determined by the quality of the employed force field and the simulation parameters. Much of the work on parametrizing and optimizing force fields for biomembrane modeling has been focused on homogeneous bilayers with a single phospholipid type. However, these may not perform effectively or could even be unsuitable for lipid mixtures commonly employed in membrane models. This work aims to fill this gap by comparing MD simulation results of several bacterial membrane models using different force fields and simulation parameters, namely, CHARMM36, Slipids, and GROMOS-CKP. Furthermore, the hydrogen isotope exchange (HIE) method, combined with GROMOS-CKP (GROMOS-H2Q), was also tested to check for the impact of this acceleration strategy on the performance of the force field. A common set of simulation parameters was employed for all of the force fields in addition to those corresponding to the original parametrization of each of them. Furthermore, new experimental order parameter values determined from NMR of several lipid mixtures are also reported to compare them with those determined from MD simulations. Our results reveal that most of the calculated physical properties of bacterial membrane models from MD simulations are substantially force field and lipid composition dependent. Some lipid mixtures exhibit nearly ideal behaviors, while the interaction of different lipid types in other mixtures is highly synergistic. None of the employed force fields seem to be clearly superior to the other three, each having its own strengths and weaknesses. Slipids are notably effective at replicating the order parameters for all acyl chains, including those in lipid mixtures, but they offer the least accurate results for headgroup parameters. Conversely, CHARMM provides almost perfect estimates for the order parameters of the headgroups but tends to overestimate those of the lipid tails. The GROMOS parametrizations deliver reasonable order parameters for entire lipid molecules, including multicomponent bilayers, although they do not reach the accuracy of Slipids for tails or CHARMM for headgroups. Importantly, GROMOS-H2Q stands out for its computational efficiency, being at least 3 times faster than GROMOS, which is already faster than both CHARMM and Slipids. In turn, GROMOS-H2Q yields much higher compressibilities compared to all other parametrizations.

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.

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.

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.

The Lord of the NanoRings: Cyclodextrins and the battle against SARS-CoV-2.

A handful of singular structures and laws can be observed in nature. They are not always evident but, once discovered, it seems obvious how to take advantage of them. In chemistry, the discovery of reproducible patterns stimulates the imagination to develop new functional materials and technological or medical applications. Two clear examples are helical structures at different levels in biological polymers as well as ring and spherical structures of different size and composition. Rings are intuitively observed as holes able to thread elongated structures. A large number of real and fictional stories have rings as inanimate protagonists. The design, development or just discovering of a special ring has often been taken as a symbol of power or success. Several examples are the Piscatory Ring wore by the Pope of the Catholic Church, the NBA Championship ring and the One Ring created by the Dark Lord Sauron in the epic story The Lord of the Rings. In this work, we reveal the power of another extremely powerful kind of rings to fight against the pandemic which is currently affecting the whole world. These rings are as small as ~1 nm of diameter and so versatile that they are able to participate in the attack of viruses, and specifically SARS-CoV-2, in a large range of different ways. This includes the encapsulation and transport of specific drugs, as adjuvants to stabilize proteins, vaccines or other molecules involved in the infection, as cholesterol trappers to destabilize the virus envelope, as carriers for RNA therapies, as direct antiviral drugs and even to rescue blood coagulation upon heparin treatment. "One ring to rule them all. One ring to find them. One ring to bring them all and in the darkness bind them." J. R. R. Tolkien.