Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A Receptor.

Characterizing the structural basis of ligand recognition of adenosine A receptor (AAR) will facilitate its rational design and development of small molecules with high affinity and selectivity, as well as optimal therapeutic effects for pain, cancers, drug abuse disorders, etc. In the present work, we applied our reported algorithm, molecular complex characterizing system (MCCS), to characterize the binding features of AAR based on its reported 3D structures of protein-ligand complexes. First, we compared the binding score to the reported experimental binding affinities of each compound. Then, we calculated an output example of residue energy contribution using MCCS and compared the results with data obtained from MM/GBSA. The consistency in results indicated that MCCS is a powerful, fast, and accurate method. Sequentially, using a receptor-ligand data set of 57 crystallized structures of AARs, we characterized the binding features of the binding pockets in AAR, summarized the key residues that distinguish antagonist from agonist, produced heatmaps of residue energy contribution for clustering various statuses of AARs, explored the selectivity between AAR and AAR, etc. All the information provided new insights into the protein features of AAR and will facilitate its rational drug design.