Gating residues govern ligand unbinding kinetics from the buried cavity in HIF-2α PAS-B.

While transcription factors have been generally perceived as "undruggable," an exception is the HIF-2 hypoxia-inducible transcription factor, which contains an internal cavity that is sufficiently large to accommodate a range of small-molecules, including the therapeutically used inhibitor belzutifan. Given the relatively long ligand residence times of these small molecules and the lack of any experimentally observed pathway connecting the cavity to solvent, there has been great interest in understanding how these drug ligands exit the buried receptor cavity. Here, we focus on the relevant PAS-B domain of hypoxia-inducible factor 2α (HIF-2α) and examine how one such small molecule (THS-017) exits from the buried cavity within this domain on the seconds-timescale using atomistic simulations and ZZ-exchange NMR.

Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin.

It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding.

Probing allosteric communication with combined molecular dynamics simulations and network analysis.

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource.

"A Feeling of Safeness and Freedom": The Promotion of Mental Health Recovery Through Co-Production in an Italian Community Organization.

In mental health promotion, recovery is a process that leads to personal strengthening, control over crucial life decisions, and participation in communities through relevant professional, educational, or family social roles. Co-production, a key aspect of the recovery-oriented approach, emphasizes collaboration and active participation of people with mental health first-hand experience, family members, and citizens. Even though studies on co-production are limited and fragmented, there is evidence that co-production leads to positive outcomes, including improved well-being, empowerment, social connectedness, inclusion, and personal competencies.

Fragment Merging, Growing, and Linking Identify New Trypanothione Reductase Inhibitors for Leishmaniasis.

Trypanothione reductase (TR) is a suitable target for drug discovery approaches against leishmaniasis, although the identification of potent inhibitors is still challenging. Herein, we harnessed a fragment-based drug discovery (FBDD) strategy to develop new TR inhibitors. Previous crystallographic screening identified fragments -, which provided ideal starting points for a medicinal chemistry campaign.

Potent Antioxidant and Anti-Tyrosinase Activity of Butein and Homobutein Probed by Molecular Kinetic and Mechanistic Studies.

Butein (BU) and homobutein (HB) are bioactive polyhydroxylated chalcones widespread in dietary plants, whose antioxidant properties require mechanistic definition. They were investigated by inhibited autoxidation kinetic studies of methyl linoleate in Triton™ X-100 micelles at pH 7.4, 37 °C.

Computational drug discovery under RNA times.

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules.

Integrated Approach Including Docking, MD Simulations, and Network Analysis Highlights the Action Mechanism of the Cardiac hERG Activator RPR260243.

hERG is a voltage-gated potassium channel involved in the heart contraction whose defections are associated with the cardiac arrhythmia Long QT Syndrome type 2. The activator RPR260243 (RPR) represents a possible candidate to pharmacologically treat LQTS2 because it enhances the opening of the channel. However, the molecular detail of its action mechanism remains quite elusive.

From Acid Activation Mechanisms of Proton Conduction to Design of Inhibitors of the M2 Proton Channel of Influenza A Virus.

With an estimated 1 billion people affected across the globe, influenza is one of the most serious health concerns worldwide. Therapeutic treatments have encompassed a number of key functional viral proteins, mainly focused on the M2 proton channel and neuraminidase. This review highlights the efforts spent in targeting the M2 proton channel, which mediates the proton transport toward the interior of the viral particle as a preliminary step leading to the release of the fusion peptide in hemagglutinin and the fusion of the viral and endosomal membranes.

Probing the transport of Ni(II) ions through the internal tunnels of the Helicobacter pylori UreDFG multimeric protein complex.

The survival of several pathogenic bacteria, such as Helicobacter pylori (Hp), relies on the activity of the nickel-dependent enzyme urease. Nickel insertion into urease is mediated by a multimeric chaperone complex (HpUreDFG) that is responsible for the transport of Ni(II) from a conserved metal binding motif located in the UreG dimer (CPH motif) to the catalytic site of the enzyme. The X-ray structure of HpUreDFG revealed the presence of water-filled tunnels that were proposed as a route for Ni(II) translocation.

Ion Conduction Mechanism as a Fingerprint of Potassium Channels.

K-channels are membrane proteins that regulate the selective conduction of potassium ions across cell membranes. Although the atomic mechanisms of K permeation have been extensively investigated, previous work focused on characterizing the selectivity and occupancy of the binding sites, the role of water molecules in the conduction process, or the identification of the minimum energy pathways enabling permeation. Here, we exploit molecular dynamics simulations and the analytical power of Markov state models to perform a comparative study of ion conduction in three distinct channel models.

Conduction and Gating Properties of the TRAAK Channel from Molecular Dynamics Simulations with Different Force Fields.

In recent years, the K2P family of potassium channels has been the subject of intense research activity. Owing to the complex function and regulation of this family of ion channels, it is common practice to complement experimental findings with the atomistic description provided by computational approaches such as molecular dynamics (MD) simulations, especially, in light of the unprecedented timescales accessible at present. However, despite recent substantial improvements, the accuracy of MD simulations is still undermined by the intrinsic limitations of force fields.

Data-Driven Molecular Dynamics: A Multifaceted Challenge.

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue.

Enhanced Molecular Dynamics Simulations of Intrinsically Disordered Proteins.

Molecular dynamics simulations represent a powerful tool to gain insights into structural and dynamical features of biomolecular systems. Nevertheless, their recognized limitation in terms of achievable timescales becomes particularly severe when dealing with slow processes. In such cases, the employment of enhanced sampling methods, which allow accelerating the characterization of rare events in a timeframe consistent with conventional computational resources, results as crucial.

Targeting the Protein Tunnels of the Urease Accessory Complex: A Theoretical Investigation.

Urease is a nickel-containing enzyme that is essential for the survival of several and often deadly pathogenic bacterial strains, including . Notwithstanding several attempts, the development of direct urease inhibitors without side effects for the human host remains, to date, elusive. The recently solved X-ray structure of the UreDFG accessory complex involved in the activation of urease opens new perspectives for structure-based drug discovery.

Exploring Conformational Dynamics of the Extracellular Domain of the GABA Receptor: A Path-Metadynamics Study.

γ-Aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system (CNS). Dysfunctional GABAergic neurotransmission is associated with numerous neurological and neuropsychiatric disorders. The GABA receptor (GABA-R) is a heterodimeric class C G protein-coupled receptor (GPCR) comprised of GABA and GABA subunits.

An Integrated Markov State Model and Path Metadynamics Approach To Characterize Drug Binding Processes.

Unveiling the mechanistic features of drug-target binding is of central interest in biophysics and drug discovery. Herein, we address this challenge by combining two major computational approaches, namely, Molecular Dynamics (MD) simulations and Markov State Models (MSM), with a Path Collective Variables (PCVs) description coupled with metadynamics. We apply our methodology to reconstruct the binding process of the antagonist alprenolol to the β-adrenergic receptor, a well-established pharmaceutical target.

Kinetics of Drug Binding and Residence Time.

The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy).

Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics.

Computational approaches currently assist medicinal chemistry through the entire drug discovery pipeline. However, while several computational tools and strategies are available to predict binding affinity, predicting the drug-target binding kinetics is still a matter of ongoing research. Here, we challenge scaled molecular dynamics simulations to assess the off-rates for a series of structurally diverse inhibitors of the heat shock protein 90 (Hsp90) covering 3 orders of magnitude in their experimental residence times.

Binding Residence Time through Scaled Molecular Dynamics: A Prospective Application to hDAAO Inhibitors.

Traditionally, a drug potency is expressed in terms of thermodynamic quantities, mostly K and empirical IC values. Although binding affinity as an estimate of drug activity remains relevant, it is increasingly clear that it is also important to include (un)binding kinetic parameters in the characterization of potential drug-like molecules. Herein, we used standard in silico screening to identify a series of structurally related inhibitors of hDAAO, a flavoprotein involved in schizophrenia and neuropathic pain.

Fully Flexible Docking via Reaction-Coordinate-Independent Molecular Dynamics Simulations.

Predicting the geometry of protein-ligand binding complexes is of primary importance for structure-based drug discovery. Molecular dynamics (MD) is emerging as a reliable computational tool for use in conjunction with, or an alternative to, docking methods. However, simulating the protein-ligand binding process often requires very expensive simulations.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery.

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power.

Structural and Kinetic Characterization of the Intrinsically Disordered Protein SeV N through Enhanced Sampling Simulations.

Intrinsically disordered proteins (IDPs) are emerging as an important class of the proteome. Being able to interact with different molecular targets, they participate in many physiological and pathological activities. However, due to their intrinsically heterogeneous nature, determining the equilibrium properties of IDPs is still a challenge for biophysics.