Subtle Chemical Changes Cross the Boundary between Agonist and Antagonist: New A3 Adenosine Receptor Homology Models and Structural Network Analysis Can Predict This Boundary.

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.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.

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.