five

Atomistic Models of General Anesthetics for Use in in Silico Biological Studies

收藏
NIAID Data Ecosystem2026-03-09 收录
下载链接:
https://figshare.com/articles/dataset/Atomistic_Models_of_General_Anesthetics_for_Use_in_in_Silico_Biological_Studies/2042637
下载链接
链接失效反馈
官方服务:
资源简介:
While small molecules have been used to induce anesthesia in a clinical setting for well over a century, a detailed understanding of the molecular mechanism remains elusive. In this study, we utilize ab initio calculations to develop a novel set of CHARMM-compatible parameters for the ubiquitous modern anesthetics desflurane, isoflurane, sevoflurane, and propofol for use in molecular dynamics (MD) simulations. The parameters generated were rigorously tested against known experimental physicochemical properties including dipole moment, density, enthalpy of vaporization, and free energy of solvation. In all cases, the anesthetic parameters were able to reproduce experimental measurements, signifying the robustness and accuracy of the atomistic models developed. The models were then used to study the interaction of anesthetics with the membrane. Calculation of the potential of mean force for inserting the molecules into a POPC bilayer revealed a distinct energetic minimum of 4–5 kcal/mol relative to aqueous solution at the level of the glycerol backbone in the membrane. The location of this minimum within the membrane suggests that anesthetics partition to the membrane prior to binding their ion channel targets, giving context to the Meyer–Overton correlation. Moreover, MD simulations of these drugs in the membrane give rise to computed membrane structural parameters, including atomic distribution, deuterium order parameters, dipole potential, and lateral stress profile, that indicate partitioning of anesthetics into the membrane at the concentration range studied here, which does not appear to perturb the structural integrity of the lipid bilayer. These results signify that an indirect, membrane-mediated mechanism of channel modulation is unlikely.
创建时间:
2015-12-17
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作