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.

Lipidomics signature in post-COVID patient sera and its influence on the prolonged inflammatory response

BACKGROUND

The ongoing issues with post-COVID conditions (PCC), where symptoms persist long after the initial infection, highlight the need for research into blood lipid changes in these patients. While most studies focus on the acute phase of COVID-19, there's a significant lack of information on the lipidomic changes that occur in the later stages of the disease. Addressing this knowledge gap is critical for understanding the long-term effects of COVID-19 and could be key to developing personalized treatments for those suffering from PCC.

METHODS

We employed untargeted lipidomics to analyze plasma samples from 147 PCC patients, assessing nearly 400 polar lipids. Data mining (DM) and machine learning (ML) tools were utilized to decode the results and ascertain significant lipidomic patterns.

RESULTS

The study uncovered substantial changes in various lipid subclasses, presenting a detailed profile of the polar lipid fraction in PCC patients. These alterations correlated with ongoing inflammation and immune response. Notably, there were elevated levels of lysophosphatidylglycerols (LPGs) and phosphatidylethanolamines (PEs), and reduced levels of lysophosphatidylcholines (LPCs), suggesting these as potential lipid biomarkers for PCC. The lipidomic signatures indicated specific anionic lipid changes, implicating antimicrobial peptides (AMPs) in inflammation. Associations between particular medications and symptoms were also suggested. Classification models, such as multinomial regression (MR) and random forest (RF), successfully differentiated between symptomatic and asymptomatic PCC groups using lipidomic profiles.

CONCLUSIONS

The study's groundbreaking discovery of specific lipidomic disruptions in PCC patients marks a significant stride in the quest to comprehend and combat this condition. The identified lipid biomarkers not only pave the way for novel diagnostic tools but also hold the promise to tailor individualized therapeutic strategies, potentially revolutionizing the clinical approach to managing PCC and improving patient care.

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.

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).

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.

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/.

Aggregation versus inclusion complexes to solubilize drugs with cyclodextrins. A case study using sulphobutylether-β-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-β-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-β-cyclodextrins, broadly used as an excipient in many pharmaceutical formulations, in particular in the case of remdesivir as an active compound.

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.

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.

Delving Into the Origin of Destructive Inflammation in COVID-19: A Betrayal of Natural Host Defense Peptides?

In contrast to other pathogenic agents that directly destroy host cells and tissues, the lethal power of SARS-CoV-2 resides in the over-reactive immune response triggered by this virus. Based on numerous evidences indicating that the lipid composition of host membranes is dramatically affected by COVID-19, and in the fact that our endogenous antimicrobial peptides (AMPs) are sensitive to the membrane composition of pathogenic agents, we propose that such destructive immune response is due to the direct action of AMPs. In a scenario where most host cell membranes are dressed by a pathogenic lipid composition, AMPs can indiscriminately attack them. This is why we use the "AMP betrayal" term to describe this mechanism. Previously proposed cytokine/bradykinin storm mechanisms are not incompatible with this new proposal. Interestingly, the harmful action of AMPs could be prevented by new therapies aimed to reestablish the lipid composition or to inhibit the action of specific peptides.

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.

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.

Parallel Versus Antiparallel β-Sheet Structure in Cyclic Peptide Hybrids Containing γ- or δ-Cyclic Amino Acids

Cyclic peptides with disc-shaped structures have emerged as potent building blocks for the preparation of new biomaterials in fields ranging from biological to material science. In this work, we analyze in depth the self-assembling properties of a new type of cyclic peptides based on the alternation of α-residues and cyclic δ-amino acids (α,δ-CPs). To examine the preferred stacking properties adopted by cyclic peptides bearing this type of amino acids, we carried out a synergistic in vitro/in silico approximation by using simple dimeric models and then extended to nanotubes. Although these new cyclic peptides (α,δ-CPs) can interact either in a parallel or antiparallel fashion, our results confirm that although the parallel β-sheet is more stable, it can be switched to the antiparallel stacking by choosing residues that can establish favorable cross-strand interactions. Moreover, the subsequent comparison by using the same methodology but applied to α,γ-CPs models, up to the moment assumed as antiparallel-like d,l-α-CPs, led to unforeseen conclusions that put into question preliminary conjectures about these systems. Surprisingly, they tend to adopt a parallel β-sheet directed by the skeleton interactions. These results imply a change of paradigm with respect to cyclic peptide designs that should be considered for dimers and nanotubes.

Competitive double-switched self-assembled cyclic peptide nanotubes: a dual internal and external control

"Intelligent" materials based on synthetic small molecules that become functional only under specific conditions provide new opportunities for developing regulated systems aimed at a large number of applications. For instance, biologically active supramolecular entities that are sensitive to environmental conditions, such as the presence of bacterial membranes, are extremely interesting in biomedicine. In this work, we have designed and investigated, using molecular dynamics simulations, a doubly modulable nanotube formed by the self-assembly of cyclic peptides sensitive to both the presence of a lipid membrane and the pH of the aqueous media. The cyclic peptides were designed to self-assemble into peptide nanotubes in the presence of a lipid bilayer and at low pH values. Under these conditions, the residual side chains point outside the cyclic peptides, being exposed to the lipid bilayer, and the inner groups (carboxylic acids) are protonated, thus allowing the permeation of water and preventing that of ions. Higher pH values are expected to create carboxylate groups at the lumen of the peptides, leading to the disassembly of the nanotube, the attraction and translocation of ions towards the hydrophobic core of the bilayer, and eventually killing the target malignant cells. Our results suggest that by introducing a second switch in a membrane sensitive system, it is possible to modulate its interaction with the lipid bilayer. This opens the door to new strategies for the preparation of antimicrobial peptides that interact at the membrane level.

Cyclo-lib: a database of computational molecular dynamics simulations of cyclodextrins

MOTIVATION

Cyclodextrins (CDs) are amongst the most versatile/multi-functional molecules used in molecular research and chemical applications. They are natural cyclic oligosaccharides typically employed to encapsulate hydrophobic groups in their central cavity. This allows solubilizing, protecting or reducing the toxicity of a large variety of different molecules including drugs, dyes and surfactant agents. In spite of their great potential, atomic level information of these molecules, which is key for their function, is really scarce. Computational Molecular Dynamics (MD) simulations have the potential to efficiently fill this gap, providing structural-dynamic information at atomic level in time scales ranging from ps to μs.

RESULTS

Cyclo-lib is a database with a publicly accessible web-interface containing structural and dynamic analysis obtained from computational MD simulation trajectories (250 ns long) of native and modified CDs in explicit water molecules. Cyclo-lib currently includes 70 CDs typically employed for fundamental and industrial research. Tools for comparative analysis between different CDs, as well as to restrict the analysis to specific time-segments within the trajectories are also available. Cyclo-lib provides atomic resolution information aimed to complement experimental results performed with the same molecules.

AVAILABILITY AND IMPLEMENTATION

The database is freely available under http://cyclo-lib.mduse.com/ CONTACT: Angel.Pineiro@usc.es.

Membrane-targeted self-assembling cyclic peptide nanotubes

Peptide nanotubes are novel supramolecular nanobiomaterials that have a tubular structure. The stacking of cyclic components is one of the most promising strategies amongst the methods described in recent years for the preparation of nanotubes. This strategy allows precise control of the nanotube surface properties and the dimensions of the tube diameter. In addition, the incorporation of 3- aminocycloalkanecarboxylic acid residues in the nanotube-forming peptides allows control of the internal properties of the supramolecular tube. The research aimed at the application of membrane-interacting self-assembled cyclic peptide nanotubes (SCPNs) is summarized in this review. The cyclic peptides are designed to interact with phospholipid bilayers to induce nanotube formation. The properties and orientation of the nanotube can be tuned by tailoring the peptide sequence. Hydrophobic peptides form transmembrane pores with a hydrophilic orifice, the nature of which has been exploited to transport ions and small molecules efficiently. These synthetic ion channels are selective for alkali metal ions (Na(+), K(+) or Cs(+)) over divalent cations (Ca(2+)) or anions (Cl(-)). Unfortunately, selectivity was not achieved within the series of alkali metal ions, for which ion transport rates followed the diffusion rates in water. Amphipathic peptides form nanotubes that lie parallel to the membrane. Interestingly, nanotube formation takes place preferentially on the surface of bacterial membranes, thus making these materials suitable for the development of new antimicrobial agents.