Modeling the P2X7 receptor: a comparative analysis of conventional methods and AlphaFold

P2X7 is a purinergic receptor involved in inflammation, pain, and neurodegeneration and is an important pharmacological target, with no drugs approved for clinical therapy. Given the limited availability of crystallized human P2X7 (hP2X7) receptor structures, this study evaluates the structural fidelity of models generated through conventional homology modeling and advanced deep learning approaches using AlphaFold, aiming to support structure-based drug discovery.

The hP2X7 receptor was modeled through conventional homology-based methods using SWISS-MODEL guided by template and sequence alignment. Additional structural models were generated by AlphaFold 2 (AF2) and AlphaFold 3 (AF3). All four resulting models were assessed for confidence levels at orthosteric and allosteric binding sites and employed in docking simulations with adenosine triphosphate (ATP) and JNJ47965567 to evaluate their suitability for capturing key ligand-receptor interactions.

Two rat P2X7 (rP2X7) receptor structures were selected as templates for modeling the hP2X7 protein: one in an ATP-bound open conformation (Protein Data Bank, PDB ID: 6U9W) and the other in an antagonist-bound closed state (PDB ID: 8TRB). These templates yielded two homology-based models, Q99572-6U9W and Q99572-8TRB, with scores of 0.71 and 0.77, respectively, indicating great structural quality. On the other hand, AF2 and AF3 generated hP2X7 receptor models with scores of 0.841 and 0.82, respectively, reflecting high prediction confidence. However, a key limitation of AlphaFold algorithms is their ability to generate P2X7 receptor structures exclusively in the closed state, restricting their ability to model ligand-bound open conformations. Docking simulations suggest that models generated using either AF2 or comparative modeling exhibit enhanced interaction profiles with key residues at both the orthosteric and allosteric binding sites of the P2X7 receptor.

This study compared AF2 and AF3 with conventional modeling techniques for constructing hP2X7 receptor models and revealed that, compared with AF3, AF2 generated high-confidence models with strong ligand-binding potential for virtual screening (VS), despite the latter effectively modeling protein-ligand complexes. Traditional modeling remains crucial for improving accuracy in flexible or poorly resolved regions. Protein modeling advancements hold the potential to revolutionize VS, accelerating the discovery of novel P2X7 receptor antagonists.