标题： MoDyGAN：将分子动力学与生成对抗网络结合以研究蛋白质构象空间 显示英文标题

标题： MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space

摘要： 广泛探索蛋白质构象景观在计算生物学中仍然是一个重大挑战，因为动态基于物理的模拟涉及高昂的计算成本。 在本工作中，我们提出了一种新的流程 MoDyGAN，该流程利用分子动力学（MD）模拟和生成对抗网络（GANs）来探索蛋白质构象空间。 MoDyGAN 包含一个生成器，该生成器将高斯分布映射到 MD 得到的蛋白质轨迹，并包含一个精炼模块，该模块结合集成学习与双判别器以进一步提高生成构象的合理性。 我们方法的核心是一种创新的表示技术，该技术可将 3D 蛋白质结构可逆地转换为 2D 矩阵，从而可以使用先进的基于图像的 GAN 架构。 我们使用三种刚性蛋白质来证明 MoDyGAN 可以生成合理的新的构象。 我们还以十肽丙氨酸为例，展示潜在空间中的插值与从导向分子动力学（SMD）模拟中获得的轨迹高度一致。 我们的结果表明，将蛋白质表示为类似图像的数据为将先进的深度学习技术应用于生物分子模拟开辟了新可能性，从而实现了构象状态的有效采样。 此外，所提出的框架在扩展到其他复杂的 3D 结构方面具有很强的潜力。 显示英文摘要

摘要： Extensively exploring protein conformational landscapes remains a major challenge in computational biology due to the high computational cost involved in dynamic physics-based simulations. In this work, we propose a novel pipeline, MoDyGAN, that leverages molecular dynamics (MD) simulations and generative adversarial networks (GANs) to explore protein conformational spaces. MoDyGAN contains a generator that maps Gaussian distributions into MD-derived protein trajectories, and a refinement module that combines ensemble learning with a dual-discriminator to further improve the plausibility of generated conformations. Central to our approach is an innovative representation technique that reversibly transforms 3D protein structures into 2D matrices, enabling the use of advanced image-based GAN architectures. We use three rigid proteins to demonstrate that MoDyGAN can generate plausible new conformations. We also use deca-alanine as a case study to show that interpolations within the latent space closely align with trajectories obtained from steered molecular dynamics (SMD) simulations. Our results suggest that representing proteins as image-like data unlocks new possibilities for applying advanced deep learning techniques to biomolecular simulation, leading to an efficient sampling of conformational states. Additionally, the proposed framework holds strong potential for extension to other complex 3D structures.