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. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.

"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. This study aimed to contribute to the limited literature on co-production in mental health by evaluating the co-production process in a non-profit mental health organization and its impact on empowerment processes and personal recovery outcomes. The research team adopted a collaborative approach and conducted qualitative research, including 13 individual semi-structured interviews and four focus groups. Results showed how the different dimensions of empowerment are promoted in and by the organization: (a) co-production processes supported empowered outcomes on an individual level, such as self-awareness; (b) the organization was perceived to promote empowering processes, such as a sense of safeness and protection; (c) co-production was a mean to build and maintain a network with mental health services that acknowledges the dignity and value of each subjectivity and promotes participation and recovery. Peer support workers were seen as facilitators of mental illness management, and the organization as a place for sharing mental health experiences and fostering individual recovery journeys.

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. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic.

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 1-3, which provided ideal starting points for a medicinal chemistry campaign. In silico investigations revealed critical hotspots in the TR binding site, guiding our structure- and ligand-based structure-actvity relationship (SAR) exploration that yielded fragment-derived compounds 4-14. A trend of improvement in Leishmania infantum TR inhibition was detected along the optimization and confirmed by the crystal structures of 9, 10, and 14 in complex with Trypanosoma brucei TR. Compound 10 showed the best TR inhibitory profile (Ki = 0.2 µM), whereas 9 was the best one in terms of in vitro and ex vivo activity. Although further fine-tuning is needed to improve selectivity, we demonstrated the potentiality of FBDD on a classic but difficult target for leishmaniasis.

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. Butein had kinh = (3.0 ± 0.9) × 104 M-1s-1 showing a chain-breaking mechanism with higher antioxidant activity than reference α-tocopherol (kinh = (2.2 ± 0.6) × 104 M-1s-1), particularly concerning the stoichiometry or peroxyl radical trapping n = 3.7 ± 1.1 vs. 2.0 for tocopherol. Homobutein had kinh = (2.8 ± 0.9) × 103 M-1s-1, pairing the relative BDEOH measured by radical equilibration EPR as 78.4 ± 0.2 kcal/mol for BU and estimated as 82.6 kcal/mol for HB. The inhibition of mushroom tyrosinase (mTYR) by HB and BU was also investigated. BU gives a reversible uncompetitive inhibition of monophenolase reaction with KI' = 9.95 ± 2.69 µM and mixed-type diphenolase inhibition with KI = 3.30 ± 0.75 µM and KI' = 18.75 ± 5.15 µM, while HB was nearly competitive toward both mono- and diphenolase with respective KI of 2.76 ± 0.70 µM and 2.50 ± 1.56 µM. IC50 values (monophenolase/diphenolase at 1 mM substrate) were 10.88 ± 2.19 µM/15.20 ± 1.25 µM, 14.78 ± 1.05 µM/12.36 ± 2.00 µM, and 33.14 ± 5.03 µM/18.27 ± 3.42 µM, respectively, for BU, HB, and reference kojic acid. Molecular docking studies confirmed the mechanism. Results indicate very potent antioxidant activity for BU and potent anti-tyrosinase activity for both chalcones, which is discussed in relation to bioactivity toward protection from skin disorders and food oxidative spoilage.

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. Here, we address the problem using a combination of docking, molecular dynamics simulations, and network analysis. We show that the drug preferably binds at the interface between the voltage sensor and the pore, enhancing the canonical activation path and determining a whole-structure rearrangement of the channel that slightly impairs inactivation.

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. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.

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. Significant differences emerged in terms of permeation mechanisms and binding site occupancy by potassium ions and/or water molecules from 100 µs cumulative trajectories. We found that, at odds with the current paradigm, each system displays a characteristic permeation mechanism, and thus, there is not a unique way by which potassium ions move through K+-channels. The high functional diversity of K+-channels can be attributed in part to the differences in conduction features that have emerged from this work. This study provides crucial information and further inspiration for wet-lab chemists designing new synthetic strategies to produce versatile artificial ion channels that emulate membrane transport for their applications in diagnosis, sensors, the next generation of water treatment technologies, etc., as the ability of synthetic channels to transport molecular ions across a bilayer in a controlled way is usually governed through the choice of metal ions, their oxidation states, or their coordination geometries.

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. Here, we probe the transport of Ni(II) through the internal tunnels of HpUreDFG, from the CPH motif to the external surface of the complex, using microsecond-long enhanced molecular dynamics simulations. The results suggest a "bucket-brigade" mechanism whereby Ni(II) can be transported through a series of stations found along these internal pathways.

Targeting the JAK/STAT Pathway: A Combined Ligand- and Target-Based Approach.

Janus kinases (JAKs) are a family of proinflammatory enzymes able to mediate the immune responses and the inflammatory cascade by modulating multiple cytokine expressions as well as various growth factors. In the present study, the inhibition of the JAK-signal transducer and activator of transcription (STAT) signaling pathway is explored as a potential strategy for treating autoimmune and inflammatory disorders. A computationally driven approach aimed at identifying novel JAK inhibitors based on molecular topology, docking, and molecular dynamics simulations was carried out. For the best candidates selected, the inhibitory activity against JAK2 was evaluated in vitro. Two hit compounds with a novel chemical scaffold, 4 (IC50 = 0.81 µM) and 7 (IC50 = 0.64 µM), showed promising results when compared with the reference drug Tofacitinib (IC50 = 0.031 µM).

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. Besides the structural and mechanistic aspects of the M2 proton channel, attention is paid to the challenges posed by the development of efficient small molecule inhibitors and the evolution toward novel ligands and scaffolds motivated by the emergence of resistant strains.

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. Here, we systematically assessed the performance of the most popular force fields employed to study ion channels at timescales that are orders of magnitude greater than the ones accessible when these energy functions were first developed. Using 32 µs of trajectories, we investigated the dynamics of a member of the K2P ion channel family, the TRAAK channel, using two established force fields in simulations of biological systems: AMBER and CHARMM. We found that while results are comparable on the nanosecond timescales, significant inconsistencies arise at microsecond timescales.

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. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

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. In particular, such advanced techniques have proven highly valuable in the context of protein folding and, specifically, to explore the conformational ensemble spanned by intrinsically disordered proteins (IDPs). Here, we describe how to set up molecular dynamics simulations with one of these enhanced sampling approaches (namely, Parallel Tempering Metadynamics in the Well-Tempered Ensemble) using the NTAIL peptide as a test case.

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 Helicobacter pylori. 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 HpUreDFG accessory complex involved in the activation of urease opens new perspectives for structure-based drug discovery. In particular, the quaternary assembly and the presence of internal tunnels for nickel translocation offer an intriguing possibility to target the HpUreDFG complex in the search of indirect urease inhibitors. In this work, we adopted a theoretical framework to investigate such a hypothesis. Specifically, we searched for putative binding sites located at the protein-protein interfaces on the HpUreDFG complex, and we challenged their druggability through structure-based virtual screening. We show that, by virtue of the presence of tunnels, some protein-protein interfaces on the HpUreDFG complex are intrinsically well suited for hosting small molecules, and, as such, they possess good potential for future drug design endeavors.

Exploring Conformational Dynamics of the Extracellular Venus flytrap Domain of the GABAB 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 GABAB receptor (GABAB-R) is a heterodimeric class C G protein-coupled receptor (GPCR) comprised of GABAB1a/b and GABAB2 subunits. The orthosteric binding site for GABA is located in the extracellular Venus flytrap (VFT) domain of the GABAB1a/b. Knowledge about molecular mechanisms and druggable receptor conformations associated with activation is highly important to understand the receptor function and for rational drug design. Currently, the conformational changes of the receptor upon activation are not well described. On the basis of other class C members, the VFT is proposed to fluctuate between an open/inactive and closed/active state and one of these conformations is stabilized upon ligand binding. In the present study, we investigated the dynamics of the GABAB1b-R VFT in the apo form by combining unbiased molecular dynamics with path-metadynamics. Our simulations confirmed the open/inactive and closed/active state as the main conformations adopted by the receptor. Sizeable energy barriers were found between stable minima, suggesting a relatively slow interconversion. Previously undisclosed metastable states were also identified, which might hold potential for future drug discovery efforts.

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 ß2-adrenergic receptor, a well-established pharmaceutical target. The devised protocol allowed us to estimate the binding free energy and identify the minimum free energy path leading to the protein-ligand complex. In summary, we show that MSM and PCVs can be efficiently integrated to shed light upon mechanistic and energetic details underlying complex recognition processes in biological systems.

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/koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.

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. The derived computational predictions are in overall good agreement with experimental data. Aside from the estimation of exit times, unbinding pathways were assessed through dimensionality reduction techniques. The data analysis framework proposed in this work could lead to better understanding of the mechanistic aspects related to the observed kinetic behavior.