G-protein-coupled receptors (GPCRs) are the largest superfamily of human membrane proteins and represent primary targets of ~1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonist and antagonist ligands. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon binding of allosteric modulators. Mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have applied the Gaussian accelerated molecular dynamics (GaMD), Deep Learning (DL) and free energy prOfiling Workflow (GLOW) to map dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators. A total of 42 class A and B GPCRs with available high-resolution experimental structures are collected for modeling. All-atom GaMD simulations are performed on each GPCR structure with and without the modulator. Structural residue contact maps are then calculated from GaMD simulation frames and transformed into images for DL using convolutional neural network. Important residue contacts are determined with the highest contact gradients in the attention maps from DL and largest changes in the GPCR residue fluctuations upon modulator binding. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. Our GLOW analyses have revealed distinct free energy profiles of GPCRs upon binding of allosteric modulators. DL of GaMD simulations has provided a detailed picture of dynamic allostery in GPCRs, which shall greatly facilitate GPCR allosteric drug design.

Deep learning dynamic allostery of G-protein-coupled receptors

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