Anesthetic Binding Induced Motion of GABAA Receptors Revealed by Coarse-Grained Molecular Dynamics Simulations.

General anesthetics are indispensable in modern medicine because they induce a reversible loss of consciousness and sensation in humans. On the other hand, their molecular mechanisms of action have not yet been elucidated. Several studies have identified the main targets of some general anesthetics. The structures of γ-aminobutyric acid A (GABAA) receptors with the intravenous anesthetics such as propofol and etomidate have recently been determined. Although these anesthetic binding structures provide essential insights into the mechanism of action of anesthetics, the detailed molecular mechanism of how the anesthetic binding affects the Cl- permeability of GABAA receptors remains to be elucidated. In this study, we performed coarse-grained molecular dynamics simulations for GABAA receptors and analyzed the resulting simulation trajectories to investigate the effects of anesthetic binding on the motion of GABAA receptors. The results showed large structural fluctuations in GABAA receptors, correlations of motion between the amino acid residues, large amplitude motion, and autocorrelated slow motion, which were obtained by advanced statistical analyses. In addition, a comparison of the resulting trajectories in the presence or absence of the anesthetic molecules revealed a characteristic pore motion related to the gate-opening motion of GABAA receptors.

End-to-end protein-ligand complex structure generation with diffusion-based generative models.

BACKGROUND: Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods. RESULTS: We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available. CONCLUSION: The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.