New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations

• Published in: Biomolecules (Basel, Switzerland) Switzerland), 2020-05, Vol.10 (5), p.732
• Main Authors: Bolcato, Giovanni, Bissaro, Maicol, Deganutti, Giuseppe, Sturlese, Mattia, Moro, Stefano
• Format: Article
• Language: English
• Subjects: A1AR; A2AAR; Adenosine; Adenosine A1 Receptor Antagonists - chemistry; Adenosine A1 Receptor Antagonists - pharmacology; adenosine receptors; antagonist; Binding Sites; Drug receptors; GPCRs; Humans; Molecular Docking Simulation - methods; Molecular Dynamics Simulation; Pharmacological research; Protein Binding; Receptor, Adenosine A1 - chemistry; Receptor, Adenosine A1 - metabolism; selectivity; Supervised Machine Learning
• ISSN: 2218-273X; 2218-273X
• DOI: 10.3390/biom10050732

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 A (A AR) has been recently solved,providing important structural insights. In the present work, we rationalized the selectivity profile of two selective A AR and A AR 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.