Docking of picrotoxinin, EBOB, and TETS was performed with the RosettaLigand application (Meiler and Baker, 2006; Davis and Baker, 2009), which is comprised of three stages that progress from low-resolution conformational sampling and scoring to full-atom optimization. The RosettaLigand application with the Talaris2014 energy function was used for all docking procedures. In the first, low-resolution stage, the ligand is placed randomly within the binding site, and its center of mass is constrained to move within a 7-Å–diameter sphere. EBOB and picrotoxinin were placed according to their published binding sites (Chen et al., 2006; Masiulis et al., 2019) with their center of mass at the 6′ ring in the pore of the GABAA receptor. For TETS, we made the initial placements at six sites from position 0′ to position 20′ of the pore. Conformers were generated using OEChem, version 1.7.4 (OpenEye Scientific Software, Inc., Santa Fe, NM; www.eyesopen.com) and were then randomly rotated as a rigid body and scored for shape compatibility with the target protein (Hawkins et al., 2010; Hawkins and Nicholls, 2012). Please note that because of its caged structure, TETS only has one conformer. The best-scoring models were filtered by root-mean-square deviation to eliminate near duplicates, and one of the remaining models was selected at random to continue to the next stage. In the second, high-resolution stage, the Monte Carlo minimization protocol was employed, and ligand position and orientation were randomly perturbed by small 0.1-Å and 3° deviations: receptor side chains were repacked using a rotamer library; the ligand position, orientation, and torsions and protein side-chain torsions were simultaneously optimized using quasi-Newton minimization; and the end result was accepted or rejected based on the Metropolis criterion. Scoring used the full-atom Rosetta energy function with softened van der Waals repulsion. The side-chain rotamers were searched simultaneously during full repack cycles and one at a time in the rotamer trials cycles. The full repack makes ∼106 random rotamer substitutions at random positions and accepts or rejects each based on the Metropolis criterion. Rotamer trials choose the single best rotamer at a random position in the context of the current state of the rest of the system with the positions visited once each in random order. The ligand is treated as a single residue, and its input conformers serve as rotamers during this stage. During the energy minimization step, the finely sampled rotamer library and soft-repulsive energy function allow access to off-rotamer conformations. In the third and final stage, a more stringent gradient-based minimization of the ligand position, orientation, and torsions as well as receptor torsions for both side chains and backbone were used. Scoring applies the same Rosetta energy function but with a hard-repulsive van der Waals potential, which creates a more rugged energy landscape that is better at discriminating native from non-native binding modes. Fifty thousand docking trajectories were generated for each channel-ligand pair, and the top 50 structures were selected according to the interface scores between the ligand and the protein. When a ligand converged on a common pose in a low-energy state with reoccurring interactions, we considered it converged. Rosetta energies comparing the three channels states on the arbitrary scale employed for the Rosette energy units (REUs) were calculated with the updated RosettaLigand application using the REF2015 energy function.

All molecular graphics were rendered using the UCSF Chimera software (Resource for Biocomputing, Visualization, and Informatics, San Francisco, CA) (Pettersen et al., 2004). Protein Data Bank format files of the closed/resting state of the α2β3γ2 receptor with and without TETS are provided in the Supplemental Data; pdb files of all other models with ligands docked are available upon request.

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