Bilayers of mycolic acids (MAs) form the outer membrane of Mycobacterium tuberculosis that has high strength and extremely low permeability for external molecules (including antibiotics). For the first time, we were able to study them using the all-atom long-term molecular dynamic simulations (from 300 ns up to 1.2 μs) in order to investigate the conformational changes and most favorable structures of the mycobacterial membranes. The structure and properties of the membranes are crucially dependent on the initial packing of the α-mycolic acid (AMA) molecules, as well as on the presence of the secondary membrane components, keto- and methoxy mycolic acids (KMAs and MMAs). In the case of AMA-based membranes, the most labile conformation is W while other types of conformations (sU as well as sZ, eU, and eZ) are much more stable. In the multicomponent membranes, the presence of the KMA and MMA components (in the W conformation) additionally stabilizes both the W and eU conformations of AMA. The membrane in which AMA prevails in the eU conformation is much thicker and, at the same time, much denser. Such a packing of the MA molecules promotes the formation of a significantly stronger outer mycobacterial membrane that should be much more resistant to the threatening external factors.

The emergence of new drug-resistant strains of the tuberculosis pathogen Mycobacterium tuberculosis (Mtb) is a new challenge for modern medicine. Its resistance capacity is closely related to the properties of the outer membrane of the Mtb cell wall, which is a bilayer membrane formed by mycolic acids (MAs) and their derivatives. To date, the molecular mechanisms of the response of the Mtb outer membrane to external factors and, in particular, elevated temperatures have not been sufficiently studied. In this work, we consider the temperature-induced changes in the structure, ordering, and molecular mobility of bilayer MA membranes of various chemical and conformational compositions. Using all-atom long-term molecular dynamics simulations of various MA membranes, we report the kinetic parameters of temperature-dependent changes in the MA self-diffusion coefficients and conformational compositions, including the apparent activation energies of these processes, as well as the characteristic times of ordering changes and the features of phase transitions occurring over a wide range of elevated temperatures. Understanding these effects could be useful for the prevention of drug resistance and the development of membrane-targeting pharmaceuticals, as well as in the design of membrane-based materials.

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

The emergence of multi-drug-resistant tuberculosis strains poses a significant challenge to modern medicine. The development of new antituberculosis drugs is hindered by the low permeability of many active compounds through the extremely strong bacterial cell wall of mycobacteria. In order to estimate the ability of potential antimycobacterial agents to diffuse through the outer mycolate membrane, the free energy profiles, the corresponding activation barriers, and possible permeability modes of passive transport for a series of known antibiotics, modern antituberculosis drugs, and prospective active drug-like molecules were determined using molecular dynamics simulations with the all-atom force field and potential of mean-force calculations. The membranes of different chemical and conformational compositions, density, thickness, and ionization states were examined. The typical activation barriers for the low-mass molecules penetrating through the most realistic membrane model were 6–13 kcal/mol for isoniazid, pyrazinamide, and etambutol, and 19 and 25 kcal/mol for bedaquilin and rifampicin. The barriers for the ionized molecules are usually in the range of 37–63 kcal/mol. The linear regression models were derived from the obtained data, allowing one to estimate the permeability barriers from simple physicochemical parameters of the diffusing molecules, notably lipophilicity and molecular polarizability.

A two-stage GridSearch combined with active learning was employed to optimize GAP model parameters for Mg clusters, enabling reliable structural predictions in the extrapolative domain Mg_n, n > 50.

G protein-gated inwardly rectifying potassium (GIRK2) channels regulate neuronal excitability and are implicated in neurodevelopmental disorders. A rare KCNJ6 variant, G154C (hGIRK2 _G154C ), was identified in a patient with mild Keppen-Lubinsky syndrome features, contrasting with severe phenotypes linked to other selectivity filter mutations. Here we combined molecular dynamics simulations and patch-clamp electrophysiology to characterize the hGIRK2 _G154C mutant, revealing a widened selectivity filter that resulted in loss of potassium selectivity, aberrant sodium permeation, and loss of inward rectification, indicating a severe gain-of-function phenotype. An in silico and electrophysiological drug screen identified FDA-approved compounds, including nefazodone and eletriptan, that potently inhibited GIRK2 and GIRK2 _G154C through distinct blocking mechanisms. These findings elucidate the structural and functional impact of the G154C mutation and highlight potential pharmacological tools and therapeutic candidates for the treatment of GIRK2 channelopathies.