通过残差等距潜在流进行全部原子G蛋白偶联受体-配体模拟
All-Atom GPCR-Ligand Dynamics Simulation via a Residual Latent Flow Mode
AI总结：
本文提出GPCRLMD框架，利用残差等距潜在流实现高效全部原子GPCR-配体模拟，准确再现热力学性质与关键相互作用。
摘要：
G-蛋白偶联受体（GPCRs）是超过三分之一已获批药物的主要靶点，依赖复杂的构象转变来传递信号。尽管分子动力学（MD）对于阐明这一传递过程至关重要，尤其是在配体结合复合物中，传统的全部原子MD模拟在计算上是不可行的。在本文中，我们介绍了GPCRLMD，一种深度生成框架，用于高效地进行全部原子GPCR-配体模拟。GPCRLMD采用谐波先验变分自动编码器（HP-VAE）首先将复杂映射到正则化的等距潜在空间，通过物理约束保留几何拓扑。在该潜在空间中，残差潜在流采样演化轨迹，随后解码回原子坐标。通过捕捉相对位移，以初始结构为锚点，这种残差机制有效解耦了静态拓扑与动态波动。实验结果表明，GPCRLMD在GPCR-配体动力学模拟中实现了最先进的性能，忠实再现了热力学可观测量和关键配体-受体相互作用。
英文摘要：
G-protein-coupled receptors (GPCRs), which are targeted by over one-third of approved drugs, undergo intricate conformational transitions to transduce signals. While Molecular Dynamics (MD) is essential for elucidating this transduction process, particularly within ligand-bound complexes, conventional all-atom MD simulation is computationally prohibitive. In this paper, we introduce GPCRLMD, a deep generative framework for efficient all-atom GPCR-ligand simulation. GPCRLMD employs an Atom-Anchored Thermal Variational Autoencoder (AAT-VAE) to first map the complex into a regularized dimension-preserving latent space, maintaining geometric topology via physics-informed constraints. Within this latent space, a Residual Latent Flow samples evolution trajectories, which are subsequently decoded back to atomic coordinates. By capturing temporal dynamics via relative displacements anchored to the initial structure, this residual mechanism effectively decouples static topology from dynamic fluctuations. Experimental results demonstrate that GPCRLMD achieves state-of-the-art performance in GPCR-ligand dynamics simulation, faithfully reproducing ensemble observables and critical ligand-receptor interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
