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

• URL: http://arxiv.org/abs/2507.13950v1
• Date: Fri, 18 Jul 2025 14:18:28 GMT
• Title: MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space
• Authors: Jingbo Liang, Bruna Jacobson,
• Abstract summary: MoDyGAN is a pipeline that exploits molecular dynamics simulations and generative adversarial networks (GANs) to explore protein conformational spaces.<n>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.<n>Central to our approach is an innovative representation technique that reversibly transforms 3D protein structures into 2D matrices.<n>Our results suggest that representing proteins as image-like data unlocks new possibilities for applying advanced deep learning techniques to biomolecular simulation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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