Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A2A Receptor

Abstract

Characterizing the structural basis of ligand recognition of adenosine A_2A receptor (AA_2AR) 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 AA_2AR 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 AA_2ARs, we characterized the binding features of the binding pockets in AA_2AR, summarized the key residues that distinguish antagonist from agonist, produced heatmaps of residue energy contribution for clustering various statuses of AA_2ARs, explored the selectivity between AA_2AR and AA_1AR, etc. All the information provided new insights into the protein features of AA_2AR and will facilitate its rational drug design.

Keywords: AA2A R; MCCS; protein fingerprint; residue energy contribution.