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

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

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

Is the Stalk of the SARS-CoV-2 Spike Protein Druggable?

The spike protein is key to SARS-CoV-2 high infectivity because it facilitates the receptor binding domain (RBD) encounter with ACE2. As targeting subunit S1 has not yet delivered an ACE2-binding inhibitor, we have assessed the druggability of the conserved segment of the spike protein stalk within subunit S2 by means of an integrated computational approach that combines the molecular docking of an optimized library of fragments with high-throughput molecular dynamics simulations. The high propensity of the spike protein to mutate in key regions that are responsible for the recognition of the human angiotensin-converting enzyme 2 (hACE2) or for the recognition of antibodies, has made subunit S1 of the spike protein difficult to target. Despite the inherent flexibility of the stalk region, our results suggest two hidden interhelical binding sites, whose accessibility is only partially hampered by glycan residues

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

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

A Pathway Model to Understand the Evolution of Spike Protein Binding to ACE2 in SARS-CoV-2 Variants

After the SARS-CoV-2 Wuhan variant that gave rise to the pandemic, other variants named Delta, Omicron, and Omicron-2 sequentially became prevalent, with mutations spread around the viral genome, including on the spike (S) protein; in order to understand the resultant in gains in infectivity, we interrogated in silico both the equilibrium binding and the binding pathway of the virus’ receptor-binding domain (RBD) to the angiotensin-converting enzyme 2 (ACE2) receptor. We interrogated the molecular recognition between the RBD of different variants and ACE2 through supervised molecular dynamics (SuMD) and classic molecular dynamics (MD) simulations to address the effect of mutations on the possible S protein binding pathways. Our results indicate that compensation between binding pathway efficiency and stability of the complex exists for the Omicron BA.1 receptor binding domain, while Omicron BA.2′s mutations putatively improved the dynamic recognition of the ACE2 receptor, suggesting an evolutionary advantage over the previous strains