标题： γ-氨基丁酸（GABA）受体介导麻醉的蛋白质组学习 显示英文标题

标题： Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia

摘要： 麻醉剂在手术程序和治疗干预中至关重要，但它们伴随着副作用和不同的效果水平，需要新型麻醉剂以提供更精确和可控的效果。 针对γ-氨基丁酸（GABA）受体，中枢神经系统的主要抑制性受体，可以增强其抑制作用，从而可能减少副作用并提高麻醉剂的效力。 在本研究中，我们基于24种GABA受体亚型，引入了一种基于蛋白质组学的学习方法，考虑了蛋白质-蛋白质相互作用（PPI）网络中的超过4000种蛋白质和超过150万种已知结合化合物。 我们开发了一个相应的药物-靶点相互作用网络，以识别用于新型麻醉设计的潜在先导化合物。 为确保稳健的蛋白质组学学习预测，我们整理了一个数据集，包含从PPI网络中980个靶点池中选出的136个靶点。 我们采用了三种机器学习算法，整合了先进的自然语言处理（NLP）模型，如预训练的变压器和自编码器嵌入。 通过全面的筛选过程，我们评估了针对GABRA5受体的超过18万个药物候选物的副作用和再利用潜力。 此外，我们评估了这些候选物的ADMET（吸收、分布、代谢、排泄和毒性）特性，以识别具有近似最佳特性的候选物。 该方法还涉及对现有麻醉剂结构的优化。 我们的工作提出了一个创新策略，用于新麻醉药物的开发、麻醉剂使用的优化以及对潜在麻醉相关副作用的更深入了解。 显示英文摘要

摘要： Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory receptors in the central nervous system, could enhance their inhibitory action, potentially reducing side effects while improving the potency of anesthetics. In this study, we introduce a proteomic learning of GABA receptor-mediated anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins in protein-protein interaction (PPI) networks and over 1.5 millions known binding compounds. We develop a corresponding drug-target interaction network to identify potential lead compounds for novel anesthetic design. To ensure robust proteomic learning predictions, we curated a dataset comprising 136 targets from a pool of 980 targets within the PPI networks. We employed three machine learning algorithms, integrating advanced natural language processing (NLP) models such as pretrained transformer and autoencoder embeddings. Through a comprehensive screening process, we evaluated the side effects and repurposing potential of over 180,000 drug candidates targeting the GABRA5 receptor. Additionally, we assessed the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify those with near-optimal characteristics. This approach also involved optimizing the structures of existing anesthetics. Our work presents an innovative strategy for the development of new anesthetic drugs, optimization of anesthetic use, and deeper understanding of potential anesthesia-related side effects.