1. A Taxicab geometry quantification system to evaluate the performance of in silico methods: a case study on adenosine receptors ligands.
- Author
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Kuder KJ, Michalik I, Kieć-Kononowicz K, and Kolb P
- Subjects
- Adenosine A1 Receptor Agonists chemistry, Adenosine A1 Receptor Antagonists chemistry, Binding Sites drug effects, Humans, Ligands, Models, Molecular, Molecular Docking Simulation, Molecular Dynamics Simulation, Molecular Structure, Protein Binding drug effects, Receptor, Adenosine A1 ultrastructure, Receptor, Adenosine A2A ultrastructure, Receptor, Adenosine A3 ultrastructure, Structure-Activity Relationship, Protein Conformation drug effects, Receptor, Adenosine A1 chemistry, Receptor, Adenosine A2A chemistry, Receptor, Adenosine A3 chemistry
- Abstract
Among still comparatively few G protein-coupled receptors, the adenosine A
2A receptor has been co-crystallized with several ligands, agonists as well as antagonists. It can thus serve as a template with a well-described orthosteric ligand binding region for adenosine receptors. As not all subtypes have been crystallized yet, and in order to investigate the usability of homology models in this context, multiple adenosine A1 receptor (A1 AR) homology models had been previously obtained and a library of lead-like compounds had been docked. As a result, a number of potent and one selective ligand toward the intended target have been identified. However, in in vitro experimental verification studies, many ligands also bound to the A2A AR and the A3 AR subtypes. In this work we asked the question whether a classification of the ligands according to their selectivity was possible based on docking scores. Therefore, we built an A3 AR homology model and docked all previously found ligands to all three receptor subtypes. As a metric, we employed an in vitro/in silico selectivity ranking system based on taxicab geometry and obtained a classification model with reasonable separation. In the next step, the method was validated with an external library of, selective ligands with similarly good performance. This classification system might also be useful in further screens.- Published
- 2020
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