Revisiting the Allosteric Regulation of Sodium Cation on the Binding of Adenosine at the Human A2A Adenosine Receptor: Insights from Supervised Molecular Dynamics (SuMD) Simulations

One of the most intriguing findings highlighted from G protein-coupled receptor (GPCR) crystallography is the presence, in many members of class A, of a partially hydrated sodium ion in the middle of the seven transmembrane helices (7TM) bundle. In particular, the human adenosine A2A receptor (A2A AR) is the first GPCR in which a monovalent sodium ion was crystallized in a distal site from the canonical orthosteric one, corroborating, from a structural point of view, its role as a negative allosteric modulator. However, the molecular mechanism by which the sodium ion influences the recognition of the A2A AR agonists is not yet fully understood. In this study, the supervised molecular dynamics (SuMD) technique was exploited to analyse the sodium ion recognition mechanism and how its presence influences the binding of the endogenous agonist adenosine. Due to a higher degree of flexibility of the receptor extracellular (EC) vestibule, we propose the sodium-bound A2A AR as less efficient in stabilizing the adenosine during the different steps of binding

Supporting the identification of novel fragment-based positive allosteric modulators using a supervised molecular dynamics approach: A retrospective analysis considering the human A2A adenosine receptor as a key example

Structure-driven fragment-based (SDFB) approaches have provided efficient methods for the identification of novel drug candidates. This strategy has been largely applied in discovering several pharmacological ligand classes, including enzyme inhibitors, receptor antagonists and, more recently, also allosteric (positive and negative) modulators. Recently, Siegal and collaborators reported an interesting study, performed on a detergent-solubilized StaR adenosine A2A receptor, describing the existence of both fragment-like negative allosteric modulators (NAMs), and fragment-like positive allosteric modulators (PAMs). From this retrospective study, our results suggest that Supervised Molecular Dynamics (SuMD) simulations can support, on a reasonable time scale, the identification of fragment-like PAMs following their receptor recognition pathways and characterizing the possible allosteric binding sites

New insights into key determinants for adenosine 1 receptor antagonists selectivity using supervised molecular dynamics simulations

Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models. The structure of the AR subtype A1 (A1AR) has been recently solved,providing important structural insights. In the present work, we rationalized the selectivity profile of two selective A1AR and A2AAR antagonists, investigating their recognition trajectories obtained by Supervised Molecular Dynamics from an unbound state and monitoring the role of the water molecules in the binding site

Ligand-receptor recognition events decoded at molecular scale by means of molecular dynamics simulations

During the last decades, the technological evolution has been very fast and has paved the way to a wide set of theoretical approaches able to support and stimulate the experimental component of biological sciences. From this standpoint, in drug discovery and design, the ability to make working hypothesis on how a small molecule interacts with its biological target can lead to rational approaches for the developing of new drug candidates. Nowadays is possible to model the behaviour of chemical systems up to the atomistic scale, allowing retrieve insights on mechanisms behinds the ligand binding and unbinding from a receptor. Among the computational techniques available, molecular dynamics is able to take in account fundamental aspects linked to the time evolution of a biological system, such as structural flexibility and the dynamic role of water molecules in the protein binding sites. During this Ph.D. project we employed molecular dynamics in order to disclose putative binding mechanisms of several ligands: more precisely, we applied the supervised molecular dynamics (SuMD) technique to decipher the binding pathways of both allosteric modulators and agonists to the adenosine receptors subtypes (belonging to class A of G protein-coupled receptors). Interestingly, findings highlights the coexistence of different potential recognition pathways that anticipate the formation of the orthosteric intermolecular complexes, as well as the crucial role of residues located at the extracellular portion of the protein

Advances in Computational Techniques to Study GPCR-Ligand Recognition

G-protein-coupled receptors (GPCRs) are among the most intensely investigated drug targets. The recent revolutions in protein engineering and molecular modeling algorithms have overturned the research paradigm in the GPCR field. While the numerous ligand-bound X-ray structures determined have provided invaluable insights into GPCR structure and function, the development of algorithms exploiting graphics processing units (GPUs) has made the simulation of GPCRs in explicit lipid-water environments feasible within reasonable computation times. In this review we present a survey of the recent advances in structure-based drug design approaches with a particular emphasis on the elucidation of the ligand recognition process in class A GPCRs by means of membrane molecular dynamics (MD) simulations

A Supervised Molecular Dynamics Approach to Unbiased Ligand–Protein Unbinding

The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic constants is leading the computational chemistry community to develop methods for studying the mechanisms of drug binding and unbinding. From this standpoint, molecular dynamics (MD) plays an important role in delivering insight at the molecular scale. However, a known limitation of MD is that the time scales are usually far from those involved in ligand–receptor unbinding events. Here, we show that the algorithm behind supervised MD (SuMD) can simulate the dissociation mechanism of druglike small molecules while avoiding the input of any energy bias to facilitate the transition. SuMD was tested on seven different intermolecular complexes, covering four G protein-coupled receptors: the A2A and A1 adenosine receptors, the orexin 2 and the muscarinic 2 receptors, and the soluble globular enzyme epoxide hydrolase. SuMD well-described the multistep nature of ligand–receptor dissociation, rationalized previous experimental data and produced valuable working hypotheses for structure–kinetics relationships

Deciphering the Agonist Binding Mechanism to the Adenosine A1 Receptor.

Despite being among the most characterized G protein-coupled receptors (GPCRs), adenosine receptors (ARs) have always been a difficult target in drug design. To date, no agonist other than the natural effector and the diagnostic regadenoson has been approved for human use. Recently, the structure of the adenosine A1 receptor (A1R) was determined in the active, Gi protein complexed state; this has important repercussions for structure-based drug design. Here, we employed supervised molecular dynamics simulations and mutagenesis experiments to extend the structural knowledge of the binding of selective agonists to A1R. Our results identify new residues involved in the association and dissociation pathway, they suggest the binding mode of N6-cyclopentyladenosine (CPA) related ligands, and they highlight the dramatic effect that chemical modifications can have on the overall binding mechanism, paving the way for the rational development of a structure-kinetics relationship of A1R agonists.Leverhulme Trus

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes