Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.

Adenosine plays a crucial role in various pathophysiological conditions, including neuroinflammation and neurodegeneration. Neuroinflammation can be either beneficial or detrimental to the central nervous system, depending on the intensity and duration of the inflammatory response. Across a wide range of brain disorders, neuroinflammation contributes to both the onset and progression of disease. Notably, neuroinflammation is not limited to conditions primarily classified as neuroinflammatory but is also a key factor in other neurological disorders, including life-threatening neurodegenerative diseases. All four adenosine receptor subtypes (A₁, A_2A, A_2B, and A₃) are implicated, to varying degrees, in these conditions. This review aims to summarize the roles of individual adenosine receptor subtypes in neuroinflammation and neurodegenerative diseases, emphasizing their therapeutic potential. While some therapeutic applications are well-established with clinically approved drugs, others warrant further investigation due to their promising potential.

Nirmatrelvir was the first protease inhibitor (PI) specifically developed against the SARS-CoV-2 main protease (3CL ^pro /M ^pro ) and licensed for clinical use. As SARS-CoV-2 continues to spread, variants resistant to nirmatrelvir and other currently available treatments are likely to arise. This study aimed to identify and characterize mutations that confer resistance to nirmatrelvir. To safely generate M ^pro resistance mutations, we passaged a previously developed, chimeric vesicular stomatitis virus (VSV-M ^pro ) with increasing, yet suboptimal concentrations of nirmatrelvir. Using Wuhan-1 and Omicron M ^pro variants, we selected a large set of mutants. Some mutations are frequently present in GISAID, suggesting their relevance in SARS-CoV-2. The resistance phenotype of a subset of mutations was characterized against clinically available PIs (nirmatrelvir and ensitrelvir) with cell-based and biochemical assays. Moreover, we showed the putative molecular mechanism of resistance based on in silico molecular modelling. These findings have implications on the development of future generation M ^pro inhibitors, will help to understand SARS-CoV-2 protease-inhibitor-resistance mechanisms and show the relevance of specific mutations in the clinic, thereby informing treatment decisions. Teaser: Understanding how SARS-CoV-2 could counter the antiviral drug nirmatrelvir and what it means for the future of COVID-19 treatment.

Hu antigen R (HuR), an RNA‐binding protein implicated in cancer, inflammation, and neurodegenerative disorders, represents a challenging target for drug discovery due to its large solvent‐exposed RNA‐binding interface and pronounced conformational flexibility. HuR contains tandem RNA recognition motifs (RRM1 and RRM2) that undergo substantial rearrangements between an apo open state and an RNA‐bound closed conformation. Here, we investigated both states using molecular dynamics (MD) simulations. While the RNA‐bound complex maintained a stable closed architecture, the apo form displayed extensive interdomain motions, highlighting the intrinsic flexibility of HuR. Because conventional MD simulations failed to capture the open‐to‐closed transition, we developed a novel application of Supervised MD (SuMD), previously employed for intermolecular recognition processes, to characterize HuR intramolecular domain closure. Using a multistep supervision protocol based on selected interdomain residue pairs, SuMD reproduced a closed conformation resembling the experimental RNA‐bound structure and identified a plausible reclosure pathway. Per–residue interaction analyses revealed key contributions from the RRM1–RRM2 linker region, particularly Arg97, together with Arg136 and Arg147 of RRM2, consistent with mutagenesis data. These findings extend the applicability of SuMD to intramolecular conformational transitions, provide mechanistic insight into HuR domain closure, and offer a structural framework for identifying druggable conformational states for future HuR‐targeted drug discovery.