Efficient Generation of Protein and Protein–Protein Complex Dynamics via SE(3)-Parameterized Diffusion Models

Protein and protein–protein complex conformations play a critical role in biological functions, while exploring these via traditional molecular dynamics (MD) simulation is computationally expensive. Enhanced sampling methods offer improvements but remain limited by vast conformational spaces. Recently, advances in generative deep learning have provided new avenues for protein conformational sampling. To address this challenge, we propose protein trajectory diffusion (PTraj-Diff), a geometric diffusion framework designed for generating protein and protein–protein complex trajectories. PTraj-Diff simulates protein dynamics through a denoising process that iteratively reconstructs stable conformations from random noise. By parametrizing protein structures using residue-level SE(3) transformations, the model effectively captures geometric constraints and structural relationships inherent in natural proteins while introducing tensor product attention to reduce computational overhead and lower requirements for data and hardware resources. Simultaneously, we integrate a power Bert Encoder to achieve precise long-range temporal dependency. Experimental results demonstrate that PTraj-Diff efficiently explores the conformational trajectories of protein monomers and protein–protein complexes. Moreover, it is compatible with diverse conformations generated by AlphaFold3, enabling the prediction of high-quality trajectories. As deep generative modeling continues to integrate with MD simulations, this emerging approach is poised to become a powerful tool for investigating protein conformational dynamics and elucidating biological functions.