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.

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.

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.

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/.