Distinguishing compounds' agonistic or antagonistic behavior would be of great utility for the rational discovery of selective modulators. We synthesized truncated nucleoside derivatives and discovered 6c (Ki = 2.40 nM) as a potent human A3 adenosine receptor (hA3AR) agonist, and subtle chemical modification induced a shift from antagonist to agonist. We elucidated this shift by developing new hA3AR homology models that consider the pharmacological profiles of the ligands. Taken together with molecular dynamics (MD) simulation and three-dimensional (3D) structural network analysis of the receptor-ligand complex, the results indicated that the hydrogen bonding with Thr943.36 and His2727.43 could make a stable interaction between the 3'-amino group with TM3 and TM7, and the corresponding induced-fit effects may play important roles in rendering the agonistic effect. Our results provide a more precise understanding of the compounds' actions at the atomic level and a rationale for the design of new drugs with specific pharmacological profiles.
Adenosine A3 Receptor Agonists/pharmacology , Adenosine A3 Receptor Antagonists/pharmacology , Receptor, Adenosine A3/chemistry , Receptor, Adenosine A3/metabolism , Adenosine A3 Receptor Agonists/chemistry , Adenosine A3 Receptor Antagonists/chemistry , Animals , CHO Cells , Catalytic Domain , Cricetinae , Cricetulus , HEK293 Cells , Humans , Ligands , Models, Chemical , Models, Molecular , Molecular Dynamics Simulation , Protein Conformation , Structure-Activity Relationship
G protein-coupled receptors (GPCRs) are major drug targets due to their ability to facilitate signal transduction across cell membranes, a process that is vital for many physiological functions to occur. The development of computational technology provides modern tools that permit accurate studies of the structures and properties of large chemical systems, such as enzymes and GPCRs, at the molecular level. The advent of multiscale molecular modeling permits the implementation of multiple levels of theories on a system of interest, for instance, assigning chemically relevant regions to high quantum mechanics (QM) level of theory while treating the rest of the system using classical force field (molecular mechanics (MM) potential). Multiscale QM/MM molecular modeling have far-reaching applications in the rational design of GPCR drugs/ligands by affording precise ligand binding configurations through the consideration of conformational plasticity. This enables the identification of key binding site residues that could be targeted to manipulate GPCR function. This review will focus on recent applications of multiscale QM/MM molecular simulations in GPCR studies that could boost the efficiency of future structure-based drug design (SBDD) strategies.
Models, Molecular , Receptors, G-Protein-Coupled/chemistry , Humans , Ligands , Molecular Docking Simulation , Quantum Theory , Rhodopsin/metabolism
Alzheimer's disease (AD) is an incurable, progressive neurodegenerative disease whose pathogenesis cannot be defined by one single element but consists of various factors; thus, there is a call for alternative approaches to tackle the multifaceted aspects of AD. Among the potential alternative targets, we aim to focus on glutaminyl cyclase (QC), which reduces the toxic pyroform of ß-amyloid in the brains of AD patients. On the basis of a putative active conformation of the prototype inhibitor 1, a series of N-substituted thiourea, urea, and α-substituted amide derivatives were developed. The structure-activity relationship analyses indicated that conformationally restrained inhibitors demonstrated much improved QC inhibition in vitro compared to nonrestricted analogues, and several selected compounds demonstrated desirable therapeutic activity in an AD mouse model. The conformational analysis of a representative inhibitor indicated that the inhibitor appeared to maintain the Z-E conformation at the active site, as it is critical for its potent activity.
Alzheimer Disease/drug therapy , Aminoacyltransferases/antagonists & inhibitors , Anti-Anxiety Agents/pharmacology , Drug Discovery , Enzyme Inhibitors/pharmacology , Alzheimer Disease/metabolism , Aminoacyltransferases/metabolism , Animals , Anti-Anxiety Agents/chemical synthesis , Anti-Anxiety Agents/chemistry , Cell Line , Cell Survival/drug effects , Disease Models, Animal , Dose-Response Relationship, Drug , Enzyme Inhibitors/chemical synthesis , Enzyme Inhibitors/chemistry , Humans , Mice , Mice, Inbred ICR , Molecular Structure , Quantum Theory , Structure-Activity Relationship
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
Drug Discovery , Humans , Machine Learning , Molecular Dynamics Simulation , Peptidomimetics/chemistry , Protein Binding
